greater than (>) less than (<)
H 0 always has a symbol with an equal in it. H a never has a symbol with an equal in it. The choice of symbol depends on the wording of the hypothesis test. However, be aware that many researchers (including one of the co-authors in research work) use = in the null hypothesis, even with > or < as the symbol in the alternative hypothesis. This practice is acceptable because we only make the decision to reject or not reject the null hypothesis.
H 0 : No more than 30% of the registered voters in Santa Clara County voted in the primary election. p ≤ 30
H a : More than 30% of the registered voters in Santa Clara County voted in the primary election. p > 30
A medical trial is conducted to test whether or not a new medicine reduces cholesterol by 25%. State the null and alternative hypotheses.
H 0 : The drug reduces cholesterol by 25%. p = 0.25
H a : The drug does not reduce cholesterol by 25%. p ≠ 0.25
We want to test whether the mean GPA of students in American colleges is different from 2.0 (out of 4.0). The null and alternative hypotheses are:
H 0 : μ = 2.0
H a : μ ≠ 2.0
We want to test whether the mean height of eighth graders is 66 inches. State the null and alternative hypotheses. Fill in the correct symbol (=, ≠, ≥, <, ≤, >) for the null and alternative hypotheses. H 0 : μ __ 66 H a : μ __ 66
We want to test if college students take less than five years to graduate from college, on the average. The null and alternative hypotheses are:
H 0 : μ ≥ 5
H a : μ < 5
We want to test if it takes fewer than 45 minutes to teach a lesson plan. State the null and alternative hypotheses. Fill in the correct symbol ( =, ≠, ≥, <, ≤, >) for the null and alternative hypotheses. H 0 : μ __ 45 H a : μ __ 45
In an issue of U.S. News and World Report , an article on school standards stated that about half of all students in France, Germany, and Israel take advanced placement exams and a third pass. The same article stated that 6.6% of U.S. students take advanced placement exams and 4.4% pass. Test if the percentage of U.S. students who take advanced placement exams is more than 6.6%. State the null and alternative hypotheses.
H 0 : p ≤ 0.066
H a : p > 0.066
On a state driver’s test, about 40% pass the test on the first try. We want to test if more than 40% pass on the first try. Fill in the correct symbol (=, ≠, ≥, <, ≤, >) for the null and alternative hypotheses. H 0 : p __ 0.40 H a : p __ 0.40
In a hypothesis test , sample data is evaluated in order to arrive at a decision about some type of claim. If certain conditions about the sample are satisfied, then the claim can be evaluated for a population. In a hypothesis test, we: Evaluate the null hypothesis , typically denoted with H 0 . The null is not rejected unless the hypothesis test shows otherwise. The null statement must always contain some form of equality (=, ≤ or ≥) Always write the alternative hypothesis , typically denoted with H a or H 1 , using less than, greater than, or not equals symbols, i.e., (≠, >, or <). If we reject the null hypothesis, then we can assume there is enough evidence to support the alternative hypothesis. Never state that a claim is proven true or false. Keep in mind the underlying fact that hypothesis testing is based on probability laws; therefore, we can talk only in terms of non-absolute certainties.
H 0 and H a are contradictory.
ThoughtCo / Hilary Allison
In statistical analysis, the null hypothesis assumes there is no meaningful relationship between two variables. Testing the null hypothesis can tell you whether your results are due to the effect of manipulating a dependent variable or due to chance. It's often used in conjunction with an alternative hypothesis, which assumes there is, in fact, a relationship between two variables.
The null hypothesis is among the easiest hypothesis to test using statistical analysis, making it perhaps the most valuable hypothesis for the scientific method. By evaluating a null hypothesis in addition to another hypothesis, researchers can support their conclusions with a higher level of confidence. Below are examples of how you might formulate a null hypothesis to fit certain questions.
The null hypothesis states there is no relationship between the measured phenomenon (the dependent variable ) and the independent variable , which is the variable an experimenter typically controls or changes. You do not need to believe that the null hypothesis is true to test it. On the contrary, you will likely suspect there is a relationship between a set of variables. One way to prove that this is the case is to reject the null hypothesis. Rejecting a hypothesis does not mean an experiment was "bad" or that it didn't produce results. In fact, it is often one of the first steps toward further inquiry.
To distinguish it from other hypotheses , the null hypothesis is written as H 0 (which is read as “H-nought,” "H-null," or "H-zero"). A significance test is used to determine the likelihood that the results supporting the null hypothesis are not due to chance. A confidence level of 95% or 99% is common. Keep in mind, even if the confidence level is high, there is still a small chance the null hypothesis is not true, perhaps because the experimenter did not account for a critical factor or because of chance. This is one reason why it's important to repeat experiments.
To write a null hypothesis, first start by asking a question. Rephrase that question in a form that assumes no relationship between the variables. In other words, assume a treatment has no effect. Write your hypothesis in a way that reflects this.
Are teens better at math than adults? | Age has no effect on mathematical ability. |
Does taking aspirin every day reduce the chance of having a heart attack? | Taking aspirin daily does not affect heart attack risk. |
Do teens use cell phones to access the internet more than adults? | Age has no effect on how cell phones are used for internet access. |
Do cats care about the color of their food? | Cats express no food preference based on color. |
Does chewing willow bark relieve pain? | There is no difference in pain relief after chewing willow bark versus taking a placebo. |
In addition to the null hypothesis, the alternative hypothesis is also a staple in traditional significance tests . It's essentially the opposite of the null hypothesis because it assumes the claim in question is true. For the first item in the table above, for example, an alternative hypothesis might be "Age does have an effect on mathematical ability."
BMC Medical Research Methodology volume 10 , Article number: 44 ( 2010 ) Cite this article
38k Accesses
23 Citations
18 Altmetric
Metrics details
The null hypothesis significance test (NHST) is the most frequently used statistical method, although its inferential validity has been widely criticized since its introduction. In 1988, the International Committee of Medical Journal Editors (ICMJE) warned against sole reliance on NHST to substantiate study conclusions and suggested supplementary use of confidence intervals (CI). Our objective was to evaluate the extent and quality in the use of NHST and CI, both in English and Spanish language biomedical publications between 1995 and 2006, taking into account the International Committee of Medical Journal Editors recommendations, with particular focus on the accuracy of the interpretation of statistical significance and the validity of conclusions.
Original articles published in three English and three Spanish biomedical journals in three fields (General Medicine, Clinical Specialties and Epidemiology - Public Health) were considered for this study. Papers published in 1995-1996, 2000-2001, and 2005-2006 were selected through a systematic sampling method. After excluding the purely descriptive and theoretical articles, analytic studies were evaluated for their use of NHST with P-values and/or CI for interpretation of statistical "significance" and "relevance" in study conclusions.
Among 1,043 original papers, 874 were selected for detailed review. The exclusive use of P-values was less frequent in English language publications as well as in Public Health journals; overall such use decreased from 41% in 1995-1996 to 21% in 2005-2006. While the use of CI increased over time, the "significance fallacy" (to equate statistical and substantive significance) appeared very often, mainly in journals devoted to clinical specialties (81%). In papers originally written in English and Spanish, 15% and 10%, respectively, mentioned statistical significance in their conclusions.
Overall, results of our review show some improvements in statistical management of statistical results, but further efforts by scholars and journal editors are clearly required to move the communication toward ICMJE advices, especially in the clinical setting, which seems to be imperative among publications in Spanish.
Peer Review reports
The null hypothesis statistical testing (NHST) has been the most widely used statistical approach in health research over the past 80 years. Its origins dates back to 1279 [ 1 ] although it was in the second decade of the twentieth century when the statistician Ronald Fisher formally introduced the concept of "null hypothesis" H 0 - which, generally speaking, establishes that certain parameters do not differ from each other. He was the inventor of the "P-value" through which it could be assessed [ 2 ]. Fisher's P-value is defined as a conditional probability calculated using the results of a study. Specifically, the P-value is the probability of obtaining a result at least as extreme as the one that was actually observed, assuming that the null hypothesis is true. The Fisherian significance testing theory considered the p-value as an index to measure the strength of evidence against the null hypothesis in a single experiment. The father of NHST never endorsed, however, the inflexible application of the ultimately subjective threshold levels almost universally adopted later on (although the introduction of the 0.05 has his paternity also).
A few years later, Jerzy Neyman and Egon Pearson considered the Fisherian approach inefficient, and in 1928 they published an article [ 3 ] that would provide the theoretical basis of what they called hypothesis statistical testing . The Neyman-Pearson approach is based on the notion that one out of two choices has to be taken: accept the null hypothesis taking the information as a reference based on the information provided, or reject it in favor of an alternative one. Thus, one can incur one of two types of errors: a Type I error, if the null hypothesis is rejected when it is actually true, and a Type II error, if the null hypothesis is accepted when it is actually false. They established a rule to optimize the decision process, using the p-value introduced by Fisher, by setting the maximum frequency of errors that would be admissible.
The null hypothesis statistical testing, as applied today, is a hybrid coming from the amalgamation of the two methods [ 4 ]. As a matter of fact, some 15 years later, both procedures were combined to give rise to the nowadays widespread use of an inferential tool that would satisfy none of the statisticians involved in the original controversy. The present method essentially goes as follows: given a null hypothesis, an estimate of the parameter (or parameters) is obtained and used to create statistics whose distribution, under H 0 , is known. With these data the P-value is computed. Finally, the null hypothesis is rejected when the obtained P-value is smaller than a certain comparative threshold (usually 0.05) and it is not rejected if P is larger than the threshold.
The first reservations about the validity of the method began to appear around 1940, when some statisticians censured the logical roots and practical convenience of Fisher's P-value [ 5 ]. Significance tests and P-values have repeatedly drawn the attention and criticism of many authors over the past 70 years, who have kept questioning its epistemological legitimacy as well as its practical value. What remains in spite of these criticisms is the lasting legacy of researchers' unwillingness to eradicate or reform these methods.
Although there are very comprehensive works on the topic [ 6 ], we list below some of the criticisms most universally accepted by specialists.
The P-values are used as a tool to make decisions in favor of or against a hypothesis. What really may be relevant, however, is to get an effect size estimate (often the difference between two values) rather than rendering dichotomous true/false verdicts [ 7 – 11 ].
The P-value is a conditional probability of the data, provided that some assumptions are met, but what really interests the investigator is the inverse probability: what degree of validity can be attributed to each of several competing hypotheses, once that certain data have been observed [ 12 ].
The two elements that affect the results, namely the sample size and the magnitude of the effect, are inextricably linked in the value of p and we can always get a lower P-value by increasing the sample size. Thus, the conclusions depend on a factor completely unrelated to the reality studied (i.e. the available resources, which in turn determine the sample size) [ 13 , 14 ].
Those who defend the NHST often assert the objective nature of that test, but the process is actually far from being so. NHST does not ensure objectivity. This is reflected in the fact that we generally operate with thresholds that are ultimately no more than conventions, such as 0.01 or 0.05. What is more, for many years their use has unequivocally demonstrated the inherent subjectivity that goes with the concept of P, regardless of how it will be used later [ 15 – 17 ].
In practice, the NHST is limited to a binary response sorting hypotheses into "true" and "false" or declaring "rejection" or "no rejection", without demanding a reasonable interpretation of the results, as has been noted time and again for decades. This binary orthodoxy validates categorical thinking, which results in a very simplistic view of scientific activity that induces researchers not to test theories about the magnitude of effect sizes [ 18 – 20 ].
Despite the weakness and shortcomings of the NHST, they are frequently taught as if they were the key inferential statistical method or the most appropriate, or even the sole unquestioned one. The statistical textbooks, with only some exceptions, do not even mention the NHST controversy. Instead, the myth is spread that NHST is the "natural" final action of scientific inference and the only procedure for testing hypotheses. However, relevant specialists and important regulators of the scientific world advocate avoiding them.
Taking especially into account that NHST does not offer the most important information (i.e. the magnitude of an effect of interest, and the precision of the estimate of the magnitude of that effect), many experts recommend the reporting of point estimates of effect sizes with confidence intervals as the appropriate representation of the inherent uncertainty linked to empirical studies [ 21 – 25 ]. Since 1988, the International Committee of Medical Journal Editors (ICMJE, known as the Vancouver Group ) incorporates the following recommendation to authors of manuscripts submitted to medical journals: "When possible, quantify findings and present them with appropriate indicators of measurement error or uncertainty (such as confidence intervals). Avoid relying solely on statistical hypothesis testing, such as P-values, which fail to convey important information about effect size" [ 26 ].
As will be shown, the use of confidence intervals (CI), occasionally accompanied by P-values, is recommended as a more appropriate method for reporting results. Some authors have noted several shortcomings of CI long ago [ 27 ]. In spite of the fact that calculating CI could be complicated indeed, and that their interpretation is far from simple [ 28 , 29 ], authors are urged to use them because they provide much more information than the NHST and do not merit most of its criticisms of NHST [ 30 ]. While some have proposed different options (for instance, likelihood-based information theoretic methods [ 31 ], and the Bayesian inferential paradigm [ 32 ]), confidence interval estimation of effect sizes is clearly the most widespread alternative approach.
Although twenty years have passed since the ICMJE began to disseminate such recommendations, systematically ignored by the vast majority of textbooks and hardly incorporated in medical publications [ 33 ], it is interesting to examine the extent to which the NHST is used in articles published in medical journals during recent years, in order to identify what is still lacking in the process of eradicating the widespread ceremonial use that is made of statistics in health research [ 34 ]. Furthermore, it is enlightening in this context to examine whether these patterns differ between English- and Spanish-speaking worlds and, if so, to see if the changes in paradigms are occurring more slowly in Spanish-language publications. In such a case we would offer various suggestions.
In addition to assessing the adherence to the above cited statistical recommendation proposed by ICMJE relative to the use of P-values, we consider it of particular interest to estimate the extent to which the significance fallacy is present, an inertial deficiency that consists of attributing -- explicitly or not -- qualitative importance or practical relevance to the found differences simply because statistical significance was obtained.
Many authors produce misleading statements such as "a significant effect was (or was not) found" when it should be said that "a statistically significant difference was (or was not) found". A detrimental consequence of this equivalence is that some authors believe that finding out whether there is "statistical significance" or not is the aim, so that this term is then mentioned in the conclusions [ 35 ]. This means virtually nothing, except that it indicates that the author is letting a computer do the thinking. Since the real research questions are never statistical ones, the answers cannot be statistical either. Accordingly, the conversion of the dichotomous outcome produced by a NHST into a conclusion is another manifestation of the mentioned fallacy.
The general objective of the present study is to evaluate the extent and quality of use of NHST and CI, both in English- and in Spanish-language biomedical publications, between 1995 and 2006 taking into account the International Committee of Medical Journal Editors recommendations, with particular focus on accuracy regarding interpretation of statistical significance and the validity of conclusions.
We reviewed the original articles from six journals, three in English and three in Spanish, over three disjoint periods sufficiently separated from each other (1995-1996, 2000-2001, 2005-2006) as to properly describe the evolution in prevalence of the target features along the selected periods.
The selection of journals was intended to get representation for each of the following three thematic areas: clinical specialties ( Obstetrics & Gynecology and Revista Española de Cardiología) ; Public Health and Epidemiology ( International Journal of Epidemiology and Atención Primaria) and the area of general and internal medicine ( British Medical Journal and Medicina Clínica ). Five of the selected journals formally endorsed ICMJE guidelines; the remaining one ( Revista Española de Cardiología ) suggests observing ICMJE demands in relation with specific issues. We attempted to capture journal diversity in the sample by selecting general and specialty journals with different degrees of influence, resulting from their impact factors in 2007, which oscillated between 1.337 (MC) and 9.723 (BMJ). No special reasons guided us to choose these specific journals, but we opted for journals with rather large paid circulations. For instance, the Spanish Cardiology Journal is the one with the largest impact factor among the fourteen Spanish Journals devoted to clinical specialties that have impact factor and Obstetrics & Gynecology has an outstanding impact factor among the huge number of journals available for selection.
It was decided to take around 60 papers for each biennium and journal, which means a total of around 1,000 papers. As recently suggested [ 36 , 37 ], this number was not established using a conventional method, but by means of a purposive and pragmatic approach in choosing the maximum sample size that was feasible.
Systematic sampling in phases [ 38 ] was used in applying a sampling fraction equal to 60/N, where N is the number of articles, in each of the 18 subgroups defined by crossing the six journals and the three time periods. Table 1 lists the population size and the sample size for each subgroup. While the sample within each subgroup was selected with equal probability, estimates based on other subsets of articles (defined across time periods, areas, or languages) are based on samples with various selection probabilities. Proper weights were used to take into account the stratified nature of the sampling in these cases.
Forty-nine of the 1,092 selected papers were eliminated because, although the section of the article in which they were assigned could suggest they were originals, detailed scrutiny revealed that in some cases they were not. The sample, therefore, consisted of 1,043 papers. Each of them was classified into one of three categories: (1) purely descriptive papers, those designed to review or characterize the state of affairs as it exists at present, (2) analytical papers, or (3) articles that address theoretical, methodological or conceptual issues. An article was regarded as analytical if it seeks to explain the reasons behind a particular occurrence by discovering causal relationships or, even if self-classified as descriptive, it was carried out to assess cause-effect associations among variables. We classify as theoretical or methodological those articles that do not handle empirical data as such, and focus instead on proposing or assessing research methods. We identified 169 papers as purely descriptive or theoretical, which were therefore excluded from the sample. Figure 1 presents a flow chart showing the process for determining eligibility for inclusion in the sample.
Flow chart of the selection process for eligible papers .
To estimate the adherence to ICMJE recommendations, we considered whether the papers used P-values, confidence intervals, and both simultaneously. By "the use of P-values" we mean that the article contains at least one P-value, explicitly mentioned in the text or at the bottom of a table, or that it reports that an effect was considered as statistically significant . It was deemed that an article uses CI if it explicitly contained at least one confidence interval, but not when it only provides information that could allow its computation (usually by presenting both the estimate and the standard error). Probability intervals provided in Bayesian analysis were classified as confidence intervals (although conceptually they are not the same) since what is really of interest here is whether or not the authors quantify the findings and present them with appropriate indicators of the margin of error or uncertainty.
In addition we determined whether the "Results" section of each article attributed the status of "significant" to an effect on the sole basis of the outcome of a NHST (i.e., without clarifying that it is strictly statistical significance). Similarly, we examined whether the term "significant" (applied to a test) was mistakenly used as synonymous with substantive , relevant or important . The use of the term "significant effect" when it is only appropriate as a reference to a "statistically significant difference," can be considered a direct expression of the significance fallacy [ 39 ] and, as such, constitutes one way to detect the problem in a specific paper.
We also assessed whether the "Conclusions," which sometimes appear as a separate section in the paper or otherwise in the last paragraphs of the "Discussion" section mentioned statistical significance and, if so, whether any of such mentions were no more than an allusion to results.
To perform these analyses we considered both the abstract and the body of the article. To assess the handling of the significance issue, however, only the body of the manuscript was taken into account.
The information was collected by four trained observers. Every paper was assigned to two reviewers. Disagreements were discussed and, if no agreement was reached, a third reviewer was consulted to break the tie and so moderate the effect of subjectivity in the assessment.
In order to assess the reliability of the criteria used for the evaluation of articles and to effect a convergence of criteria among the reviewers, a pilot study of 20 papers from each of three journals ( Clinical Medicine , Primary Care , and International Journal of Epidemiology) was performed. The results of this pilot study were satisfactory. Our results are reported using percentages together with their corresponding confidence intervals. For sampling errors estimations, used to obtain confidence intervals, we weighted the data using the inverse of the probability of selection of each paper, and we took into account the complex nature of the sample design. These analyses were carried out with EPIDAT [ 40 ], a specialized computer program that is readily available.
A total of 1,043 articles were reviewed, of which 874 (84%) were found to be analytic, while the remainders were purely descriptive or of a theoretical and methodological nature. Five of them did not employ either P-values or CI. Consequently, the analysis was made using the remaining 869 articles.
The percentage of articles that use only P-values, without even mentioning confidence intervals, to report their results has declined steadily throughout the period analyzed (Table 2 ). The percentage decreased from approximately 41% in 1995-1996 to 21% in 2005-2006. However, it does not differ notably among journals of different languages, as shown by the estimates and confidence intervals of the respective percentages. Concerning thematic areas, it is highly surprising that most of the clinical articles ignore the recommendations of ICMJE, while for general and internal medicine papers such a problem is only present in one in five papers, and in the area of Public Health and Epidemiology it occurs only in one out of six. The use of CI alone (without P-values) has increased slightly across the studied periods (from 9% to 13%), but it is five times more prevalent in Public Health and Epidemiology journals than in Clinical ones, where it reached a scanty 3%.
While the percentage of articles referring implicitly or explicitly to significance in an ambiguous or incorrect way - that is, incurring the significance fallacy -- seems to decline steadily, the prevalence of this problem exceeds 69%, even in the most recent period. This percentage was almost the same for articles written in Spanish and in English, but it was notably higher in the Clinical journals (81%) compared to the other journals, where the problem occurs in approximately 7 out of 10 papers (Table 3 ). The kappa coefficient for measuring agreement between observers concerning the presence of the "significance fallacy" was 0.78 (CI95%: 0.62 to 0.93), which is considered acceptable in the scale of Landis and Koch [ 41 ].
The percentage of papers mentioning a numerical finding as a conclusion is similar in the three periods analyzed (Table 4 ). Concerning languages, this percentage is nearly twice as large for Spanish journals as for those published in English (approximately 21% versus 12%). And, again, the highest percentage (16%) corresponded to clinical journals.
A similar pattern is observed, although with less pronounced differences, in references to the outcome of the NHST (significant or not) in the conclusions (Table 5 ). The percentage of articles that introduce the term in the "Conclusions" does not appreciably differ between articles written in Spanish and in English. Again, the area where this insufficiency is more often present (more than 15% of articles) is the Clinical area.
There are some previous studies addressing the degree to which researchers have moved beyond the ritualistic use of NHST to assess their hypotheses. This has been examined for areas such as biology [ 42 ], organizational research [ 43 ], or psychology [ 44 – 47 ]. However, to our knowledge, no recent research has explored the pattern of use P-values and CI in medical literature and, in any case, no efforts have been made to study this problem in a way that takes into account different languages and specialties.
At first glance it is puzzling that, after decades of questioning and technical warnings, and after twenty years since the inception of ICMJE recommendation to avoid NHST, they continue being applied ritualistically and mindlessly as the dominant doctrine. Not long ago, when researchers did not observe statistically significant effects, they were unlikely to write them up and to report "negative" findings, since they knew there was a high probability that the paper would be rejected. This has changed a bit: editors are more prone to judge all findings as potentially eloquent. This is probably the frequent denunciations of the tendency for those papers presenting a significant positive result to receive more favorable publication decisions than equally well-conducted ones that report a negative or null result, the so-called publication bias [ 48 – 50 ]. This new openness is consistent with the fact that if the substantive question addressed is really relevant, the answer (whether positive or negative) will also be relevant.
Consequently, even though it was not an aim of our study, we found many examples in which statistical significance was not obtained. However, many of those negative results were reported with a comment of this type: " The results did not show a significant difference between groups; however, with a larger sample size, this difference would have probably proved to be significant ". The problem with this statement is that it is true; more specifically, it will always be true and it is, therefore, sterile. It is not fortuitous that one never encounters the opposite, and equally tautological, statement: " A significant difference between groups has been detected; however, perhaps with a smaller sample size, this difference would have proved to be not significant" . Such a double standard is itself an unequivocal sign of the ritual application of NHST.
Although the declining rates of NHST usage show that, gradually, ICMJE and similar recommendations are having a positive impact, most of the articles in the clinical setting still considered NHST as the final arbiter of the research process. Moreover, it appears that the improvement in the situation is mostly formal, and the percentage of articles that fall into the significance fallacy is huge.
The contradiction between what has been conceptually recommended and the common practice is sensibly less acute in the area of Epidemiology and Public Health, but the same pattern was evident everywhere in the mechanical way of applying significance tests. Nevertheless, the clinical journals remain the most unmoved by the recommendations.
The ICMJE recommendations are not cosmetic statements but substantial ones, and the vigorous exhortations made by outstanding authorities [ 51 ] are not mere intellectual exercises due to ingenious and inopportune methodologists, but rather they are very serious epistemological warnings.
In some cases, the role of CI is not as clearly suitable (e.g. when estimating multiple regression coefficients or because effect sizes are not available for some research designs [ 43 , 52 ]), but when it comes to estimating, for example, an odds ratio or a rates difference, the advantage of using CI instead of P values is very clear, since in such cases it is obvious that the goal is to assess what has been called the "effect size."
The inherent resistance to change old paradigms and practices that have been entrenched for decades is always high. Old habits die hard. The estimates and trends outlined are entirely consistent with Alvan Feinstein's warning 25 years ago: "Because the history of medical research also shows a long tradition of maintaining loyalty to established doctrines long after the doctrines had been discredited, or shown to be valueless, we cannot expect a sudden change in this medical policy merely because it has been denounced by leading connoisseurs of statistics [ 53 ]".
It is possible, however, that the nature of the problem has an external explanation: it is likely that some editors prefer to "avoid troubles" with the authors and vice versa, thus resorting to the most conventional procedures. Many junior researchers believe that it is wise to avoid long back-and-forth discussions with reviewers and editors. In general, researchers who want to appear in print and survive in a publish-or-perish environment are motivated by force, fear, and expedience in their use of NHST [ 54 ]. Furthermore, it is relatively natural that simple researchers use NHST when they take into account that some theoretical objectors have used this statistical analysis in empirical studies, published after the appearance of their own critiques [ 55 ].
For example, Journal of the American Medical Association published a bibliometric study [ 56 ] discussing the impact of statisticians' co-authorship of medical papers on publication decisions by two major high-impact journals: British Medical Journal and Annals of Internal Medicine . The data analysis is characterized by methodological orthodoxy. The authors just use chi-square tests without any reference to CI, although the NHST had been repeatedly criticized over the years by two of the authors:
Douglas Altman, an early promoter of confidence intervals as an alternative [ 57 ], and Steve Goodman, a critic of NHST from a Bayesian perspective [ 58 ]. Individual authors, however, cannot be blamed for broader institutional problems and systemic forces opposed to change.
The present effort is certainly partial in at least two ways: it is limited to only six specific journals and to three biennia. It would be therefore highly desirable to improve it by studying the problem in a more detailed way (especially by reviewing more journals with different profiles), and continuing the review of prevailing patterns and trends.
Curran-Everett D: Explorations in statistics: hypothesis tests and P values. Adv Physiol Educ. 2009, 33: 81-86. 10.1152/advan.90218.2008.
Article PubMed Google Scholar
Fisher RA: Statistical Methods for Research Workers. 1925, Edinburgh: Oliver & Boyd
Google Scholar
Neyman J, Pearson E: On the use and interpretation of certain test criteria for purposes of statistical inference. Biometrika. 1928, 20: 175-240.
Silva LC: Los laberintos de la investigación biomédica. En defensa de la racionalidad para la ciencia del siglo XXI. 2009, Madrid: Díaz de Santos
Berkson J: Test of significance considered as evidence. J Am Stat Assoc. 1942, 37: 325-335. 10.2307/2279000.
Article Google Scholar
Nickerson RS: Null hypothesis significance testing: A review of an old and continuing controversy. Psychol Methods. 2000, 5: 241-301. 10.1037/1082-989X.5.2.241.
Article CAS PubMed Google Scholar
Rozeboom WW: The fallacy of the null hypothesissignificance test. Psychol Bull. 1960, 57: 418-428. 10.1037/h0042040.
Callahan JL, Reio TG: Making subjective judgments in quantitative studies: The importance of using effect sizes and confidenceintervals. HRD Quarterly. 2006, 17: 159-173.
Nakagawa S, Cuthill IC: Effect size, confidence interval and statistical significance: a practical guide for biologists. Biol Rev. 2007, 82: 591-605. 10.1111/j.1469-185X.2007.00027.x.
Breaugh JA: Effect size estimation: factors to consider and mistakes to avoid. J Manage. 2003, 29: 79-97. 10.1177/014920630302900106.
Thompson B: What future quantitative social science research could look like: confidence intervals for effect sizes. Educ Res. 2002, 31: 25-32.
Matthews RA: Significance levels for the assessment of anomalous phenomena. Journal of Scientific Exploration. 1999, 13: 1-7.
Savage IR: Nonparametric statistics. J Am Stat Assoc. 1957, 52: 332-333.
Silva LC, Benavides A, Almenara J: El péndulo bayesiano: Crónica de una polémica estadística. Llull. 2002, 25: 109-128.
Goodman SN, Royall R: Evidence and scientific research. Am J Public Health. 1988, 78: 1568-1574. 10.2105/AJPH.78.12.1568.
Article CAS PubMed PubMed Central Google Scholar
Berger JO, Berry DA: Statistical analysis and the illusion of objectivity. Am Sci. 1988, 76: 159-165.
Hurlbert SH, Lombardi CM: Final collapse of the Neyman-Pearson decision theoretic framework and rise of the neoFisherian. Ann Zool Fenn. 2009, 46: 311-349.
Fidler F, Thomason N, Cumming G, Finch S, Leeman J: Editors can lead researchers to confidence intervals but they can't make them think: Statistical reform lessons from Medicine. Psychol Sci. 2004, 15: 119-126. 10.1111/j.0963-7214.2004.01502008.x.
Balluerka N, Vergara AI, Arnau J: Calculating the main alternatives to null-hypothesis-significance testing in between-subject experimental designs. Psicothema. 2009, 21: 141-151.
Cumming G, Fidler F: Confidence intervals: Better answers to better questions. J Psychol. 2009, 217: 15-26.
Jones LV, Tukey JW: A sensible formulation of the significance test. Psychol Methods. 2000, 5: 411-414. 10.1037/1082-989X.5.4.411.
Dixon P: The p-value fallacy and how to avoid it. Can J Exp Psychol. 2003, 57: 189-202.
Nakagawa S, Cuthill IC: Effect size, confidence interval and statistical significance: a practical guide for biologists. Biol Rev Camb Philos Soc. 2007, 82: 591-605. 10.1111/j.1469-185X.2007.00027.x.
Brandstaetter E: Confidence intervals as an alternative to significance testing. MPR-Online. 2001, 4: 33-46.
Masson ME, Loftus GR: Using confidence intervals for graphically based data interpretation. Can J Exp Psychol. 2003, 57: 203-220.
International Committee of Medical Journal Editors: Uniform requirements for manuscripts submitted to biomedical journals. Update October 2008. Accessed July 11, 2009, [ http://www.icmje.org ]
Feinstein AR: P-Values and Confidence Intervals: two sides of the same unsatisfactory coin. J Clin Epidemiol. 1998, 51: 355-360. 10.1016/S0895-4356(97)00295-3.
Haller H, Kraus S: Misinterpretations of significance: A problem students share with their teachers?. MRP-Online. 2002, 7: 1-20.
Gigerenzer G, Krauss S, Vitouch O: The null ritual: What you always wanted to know about significance testing but were afraid to ask. The Handbook of Methodology for the Social Sciences. Edited by: Kaplan D. 2004, Thousand Oaks, CA: Sage Publications, Chapter 21: 391-408.
Curran-Everett D, Taylor S, Kafadar K: Fundamental concepts in statistics: elucidation and illustration. J Appl Physiol. 1998, 85: 775-786.
CAS PubMed Google Scholar
Royall RM: Statistical evidence: a likelihood paradigm. 1997, Boca Raton: Chapman & Hall/CRC
Goodman SN: Of P values and Bayes: A modest proposal. Epidemiology. 2001, 12: 295-297. 10.1097/00001648-200105000-00006.
Sarria M, Silva LC: Tests of statistical significance in three biomedical journals: a critical review. Rev Panam Salud Publica. 2004, 15: 300-306.
Silva LC: Una ceremonia estadística para identificar factores de riesgo. Salud Colectiva. 2005, 1: 322-329.
Goodman SN: Toward Evidence-Based Medical Statistics 1: The p Value Fallacy. Ann Intern Med. 1999, 130: 995-1004.
Schulz KF, Grimes DA: Sample size calculations in randomised clinical trials: mandatory and mystical. Lancet. 2005, 365: 1348-1353. 10.1016/S0140-6736(05)61034-3.
Bacchetti P: Current sample size conventions: Flaws, harms, and alternatives. BMC Med. 2010, 8: 17-10.1186/1741-7015-8-17.
Article PubMed PubMed Central Google Scholar
Silva LC: Diseño razonado de muestras para la investigación sanitaria. 2000, Madrid: Díaz de Santos
Barnett ML, Mathisen A: Tyranny of the p-value: The conflict between statistical significance and common sense. J Dent Res. 1997, 76: 534-536. 10.1177/00220345970760010201.
Santiago MI, Hervada X, Naveira G, Silva LC, Fariñas H, Vázquez E, Bacallao J, Mújica OJ: [The Epidat program: uses and perspectives] [letter]. Pan Am J Public Health. 2010, 27: 80-82. Spanish.
Landis JR, Koch GG: The measurement of observer agreement for categorical data. Biometrics. 1977, 33: 159-74. 10.2307/2529310.
Fidler F, Burgman MA, Cumming G, Buttrose R, Thomason N: Impact of criticism of null-hypothesis significance testing on statistical reporting practices in conservation biology. Conserv Biol. 2005, 20: 1539-1544. 10.1111/j.1523-1739.2006.00525.x.
Kline RB: Beyond significance testing: Reforming data analysis methods in behavioral research. 2004, Washington, DC: American Psychological Association
Book Google Scholar
Curran-Everett D, Benos DJ: Guidelines for reporting statistics in journals published by the American Physiological Society: the sequel. Adv Physiol Educ. 2007, 31: 295-298. 10.1152/advan.00022.2007.
Hubbard R, Parsa AR, Luthy MR: The spread of statistical significance testing: The case of the Journal of Applied Psychology. Theor Psychol. 1997, 7: 545-554. 10.1177/0959354397074006.
Vacha-Haase T, Nilsson JE, Reetz DR, Lance TS, Thompson B: Reporting practices and APA editorial policies regarding statistical significance and effect size. Theor Psychol. 2000, 10: 413-425. 10.1177/0959354300103006.
Krueger J: Null hypothesis significance testing: On the survival of a flawed method. Am Psychol. 2001, 56: 16-26. 10.1037/0003-066X.56.1.16.
Rising K, Bacchetti P, Bero L: Reporting Bias in Drug Trials Submitted to the Food and Drug Administration: Review of Publication and Presentation. PLoS Med. 2008, 5: e217-10.1371/journal.pmed.0050217. doi:10.1371/journal.pmed.0050217
Sridharan L, Greenland L: Editorial policies and publication bias the importance of negative studies. Arch Intern Med. 2009, 169: 1022-1023. 10.1001/archinternmed.2009.100.
Falagas ME, Alexiou VG: The top-ten in journal impact factor manipulation. Arch Immunol Ther Exp (Warsz). 2008, 56: 223-226. 10.1007/s00005-008-0024-5.
Rothman K: Writing for Epidemiology. Epidemiology. 1998, 9: 98-104. 10.1097/00001648-199805000-00019.
Fidler F: The fifth edition of the APA publication manual: Why its statistics recommendations are so controversial. Educ Psychol Meas. 2002, 62: 749-770. 10.1177/001316402236876.
Feinstein AR: Clinical epidemiology: The architecture of clinical research. 1985, Philadelphia: W.B. Saunders Company
Orlitzky M: Institutionalized dualism: statistical significance testing as myth and ceremony. Accessed Feb 8, 2010, [ http://ssrn.com/abstract=1415926 ]
Greenwald AG, González R, Harris RJ, Guthrie D: Effect sizes and p-value. What should be reported and what should be replicated?. Psychophysiology. 1996, 33: 175-183. 10.1111/j.1469-8986.1996.tb02121.x.
Altman DG, Goodman SN, Schroter S: How statistical expertise is used in medical research. J Am Med Assoc. 2002, 287: 2817-2820. 10.1001/jama.287.21.2817.
Gardner MJ, Altman DJ: Statistics with confidence. Confidence intervals and statistical guidelines. 1992, London: BMJ
Goodman SN: P Values, Hypothesis Tests and Likelihood: implications for epidemiology of a neglected historical debate. Am J Epidemiol. 1993, 137: 485-496.
The pre-publication history for this paper can be accessed here: http://www.biomedcentral.com/1471-2288/10/44/prepub
Download references
The authors would like to thank Tania Iglesias-Cabo and Vanesa Alvarez-González for their help with the collection of empirical data and their participation in an earlier version of the paper. The manuscript has benefited greatly from thoughtful, constructive feedback by Carlos Campillo-Artero, Tom Piazza and Ann Séror.
Authors and affiliations.
Centro Nacional de Investigación de Ciencias Médicas, La Habana, Cuba
Luis Carlos Silva-Ayçaguer
Unidad de Investigación. Hospital de Cabueñes, Servicio de Salud del Principado de Asturias (SESPA), Gijón, Spain
Patricio Suárez-Gil
CIBER Epidemiología y Salud Pública (CIBERESP), Spain and Departamento de Medicina, Unidad de Epidemiología Molecular del Instituto Universitario de Oncología, Universidad de Oviedo, Spain
Ana Fernández-Somoano
You can also search for this author in PubMed Google Scholar
Correspondence to Patricio Suárez-Gil .
Competing interests.
The authors declare that they have no competing interests.
LCSA designed the study, wrote the paper and supervised the whole process; PSG coordinated the data extraction and carried out statistical analysis, as well as participated in the editing process; AFS extracted the data and participated in the first stage of statistical analysis; all authors contributed to and revised the final manuscript.
Below are the links to the authors’ original submitted files for images.
Rights and permissions.
Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reprints and permissions
Cite this article.
Silva-Ayçaguer, L.C., Suárez-Gil, P. & Fernández-Somoano, A. The null hypothesis significance test in health sciences research (1995-2006): statistical analysis and interpretation. BMC Med Res Methodol 10 , 44 (2010). https://doi.org/10.1186/1471-2288-10-44
Download citation
Received : 29 December 2009
Accepted : 19 May 2010
Published : 19 May 2010
DOI : https://doi.org/10.1186/1471-2288-10-44
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative
ISSN: 1471-2288
null hypothesis
Examples of null hypothesis in a sentence.
These examples are programmatically compiled from various online sources to illustrate current usage of the word 'null hypothesis.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.
1935, in the meaning defined above
Nullarbor Plain
“Null hypothesis.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/null%20hypothesis. Accessed 14 Aug. 2024.
Britannica.com: Encyclopedia article about null hypothesis
Subscribe to America's largest dictionary and get thousands more definitions and advanced search—ad free!
Word of the day.
See Definitions and Examples »
Get Word of the Day daily email!
Plural and possessive names: a guide, commonly misspelled words, how to use em dashes (—), en dashes (–) , and hyphens (-), absent letters that are heard anyway, how to use accents and diacritical marks, popular in wordplay, 8 words for lesser-known musical instruments, it's a scorcher words for the summer heat, 7 shakespearean insults to make life more interesting, 10 words from taylor swift songs (merriam's version), 9 superb owl words, games & quizzes.
the statement postulating an experiment will find no variations between the control and experimental states, which is, no union between variants. Statistical tests are rendered to experimental outcomes in effort to disprove or refute the previously established significance level .
Your email address will not be published. Required fields are marked *
Popular psychology terms, medical model, hypermnesia, affirmation, brainwashing, backup reinforcer, affiliative behavior, message-learning approach, gross motor, behavioral modeling.
An official website of the United States government
The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.
The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.
Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .
Luis carlos silva-ayçaguer.
1 Centro Nacional de Investigación de Ciencias Médicas, La Habana, Cuba
2 Unidad de Investigación. Hospital de Cabueñes, Servicio de Salud del Principado de Asturias (SESPA), Gijón, Spain
3 CIBER Epidemiología y Salud Pública (CIBERESP), Spain and Departamento de Medicina, Unidad de Epidemiología Molecular del Instituto Universitario de Oncología, Universidad de Oviedo, Spain
The null hypothesis significance test (NHST) is the most frequently used statistical method, although its inferential validity has been widely criticized since its introduction. In 1988, the International Committee of Medical Journal Editors (ICMJE) warned against sole reliance on NHST to substantiate study conclusions and suggested supplementary use of confidence intervals (CI). Our objective was to evaluate the extent and quality in the use of NHST and CI, both in English and Spanish language biomedical publications between 1995 and 2006, taking into account the International Committee of Medical Journal Editors recommendations, with particular focus on the accuracy of the interpretation of statistical significance and the validity of conclusions.
Original articles published in three English and three Spanish biomedical journals in three fields (General Medicine, Clinical Specialties and Epidemiology - Public Health) were considered for this study. Papers published in 1995-1996, 2000-2001, and 2005-2006 were selected through a systematic sampling method. After excluding the purely descriptive and theoretical articles, analytic studies were evaluated for their use of NHST with P-values and/or CI for interpretation of statistical "significance" and "relevance" in study conclusions.
Among 1,043 original papers, 874 were selected for detailed review. The exclusive use of P-values was less frequent in English language publications as well as in Public Health journals; overall such use decreased from 41% in 1995-1996 to 21% in 2005-2006. While the use of CI increased over time, the "significance fallacy" (to equate statistical and substantive significance) appeared very often, mainly in journals devoted to clinical specialties (81%). In papers originally written in English and Spanish, 15% and 10%, respectively, mentioned statistical significance in their conclusions.
Overall, results of our review show some improvements in statistical management of statistical results, but further efforts by scholars and journal editors are clearly required to move the communication toward ICMJE advices, especially in the clinical setting, which seems to be imperative among publications in Spanish.
The null hypothesis statistical testing (NHST) has been the most widely used statistical approach in health research over the past 80 years. Its origins dates back to 1279 [ 1 ] although it was in the second decade of the twentieth century when the statistician Ronald Fisher formally introduced the concept of "null hypothesis" H 0 - which, generally speaking, establishes that certain parameters do not differ from each other. He was the inventor of the "P-value" through which it could be assessed [ 2 ]. Fisher's P-value is defined as a conditional probability calculated using the results of a study. Specifically, the P-value is the probability of obtaining a result at least as extreme as the one that was actually observed, assuming that the null hypothesis is true. The Fisherian significance testing theory considered the p-value as an index to measure the strength of evidence against the null hypothesis in a single experiment. The father of NHST never endorsed, however, the inflexible application of the ultimately subjective threshold levels almost universally adopted later on (although the introduction of the 0.05 has his paternity also).
A few years later, Jerzy Neyman and Egon Pearson considered the Fisherian approach inefficient, and in 1928 they published an article [ 3 ] that would provide the theoretical basis of what they called hypothesis statistical testing . The Neyman-Pearson approach is based on the notion that one out of two choices has to be taken: accept the null hypothesis taking the information as a reference based on the information provided, or reject it in favor of an alternative one. Thus, one can incur one of two types of errors: a Type I error, if the null hypothesis is rejected when it is actually true, and a Type II error, if the null hypothesis is accepted when it is actually false. They established a rule to optimize the decision process, using the p-value introduced by Fisher, by setting the maximum frequency of errors that would be admissible.
The null hypothesis statistical testing, as applied today, is a hybrid coming from the amalgamation of the two methods [ 4 ]. As a matter of fact, some 15 years later, both procedures were combined to give rise to the nowadays widespread use of an inferential tool that would satisfy none of the statisticians involved in the original controversy. The present method essentially goes as follows: given a null hypothesis, an estimate of the parameter (or parameters) is obtained and used to create statistics whose distribution, under H 0 , is known. With these data the P-value is computed. Finally, the null hypothesis is rejected when the obtained P-value is smaller than a certain comparative threshold (usually 0.05) and it is not rejected if P is larger than the threshold.
The first reservations about the validity of the method began to appear around 1940, when some statisticians censured the logical roots and practical convenience of Fisher's P-value [ 5 ]. Significance tests and P-values have repeatedly drawn the attention and criticism of many authors over the past 70 years, who have kept questioning its epistemological legitimacy as well as its practical value. What remains in spite of these criticisms is the lasting legacy of researchers' unwillingness to eradicate or reform these methods.
Although there are very comprehensive works on the topic [ 6 ], we list below some of the criticisms most universally accepted by specialists.
• The P-values are used as a tool to make decisions in favor of or against a hypothesis. What really may be relevant, however, is to get an effect size estimate (often the difference between two values) rather than rendering dichotomous true/false verdicts [ 7 - 11 ].
• The P-value is a conditional probability of the data, provided that some assumptions are met, but what really interests the investigator is the inverse probability: what degree of validity can be attributed to each of several competing hypotheses, once that certain data have been observed [ 12 ].
• The two elements that affect the results, namely the sample size and the magnitude of the effect, are inextricably linked in the value of p and we can always get a lower P-value by increasing the sample size. Thus, the conclusions depend on a factor completely unrelated to the reality studied (i.e. the available resources, which in turn determine the sample size) [ 13 , 14 ].
• Those who defend the NHST often assert the objective nature of that test, but the process is actually far from being so. NHST does not ensure objectivity. This is reflected in the fact that we generally operate with thresholds that are ultimately no more than conventions, such as 0.01 or 0.05. What is more, for many years their use has unequivocally demonstrated the inherent subjectivity that goes with the concept of P, regardless of how it will be used later [ 15 - 17 ].
• In practice, the NHST is limited to a binary response sorting hypotheses into "true" and "false" or declaring "rejection" or "no rejection", without demanding a reasonable interpretation of the results, as has been noted time and again for decades. This binary orthodoxy validates categorical thinking, which results in a very simplistic view of scientific activity that induces researchers not to test theories about the magnitude of effect sizes [ 18 - 20 ].
Despite the weakness and shortcomings of the NHST, they are frequently taught as if they were the key inferential statistical method or the most appropriate, or even the sole unquestioned one. The statistical textbooks, with only some exceptions, do not even mention the NHST controversy. Instead, the myth is spread that NHST is the "natural" final action of scientific inference and the only procedure for testing hypotheses. However, relevant specialists and important regulators of the scientific world advocate avoiding them.
Taking especially into account that NHST does not offer the most important information (i.e. the magnitude of an effect of interest, and the precision of the estimate of the magnitude of that effect), many experts recommend the reporting of point estimates of effect sizes with confidence intervals as the appropriate representation of the inherent uncertainty linked to empirical studies [ 21 - 25 ]. Since 1988, the International Committee of Medical Journal Editors (ICMJE, known as the Vancouver Group ) incorporates the following recommendation to authors of manuscripts submitted to medical journals: "When possible, quantify findings and present them with appropriate indicators of measurement error or uncertainty (such as confidence intervals). Avoid relying solely on statistical hypothesis testing, such as P-values, which fail to convey important information about effect size" [ 26 ].
As will be shown, the use of confidence intervals (CI), occasionally accompanied by P-values, is recommended as a more appropriate method for reporting results. Some authors have noted several shortcomings of CI long ago [ 27 ]. In spite of the fact that calculating CI could be complicated indeed, and that their interpretation is far from simple [ 28 , 29 ], authors are urged to use them because they provide much more information than the NHST and do not merit most of its criticisms of NHST [ 30 ]. While some have proposed different options (for instance, likelihood-based information theoretic methods [ 31 ], and the Bayesian inferential paradigm [ 32 ]), confidence interval estimation of effect sizes is clearly the most widespread alternative approach.
Although twenty years have passed since the ICMJE began to disseminate such recommendations, systematically ignored by the vast majority of textbooks and hardly incorporated in medical publications [ 33 ], it is interesting to examine the extent to which the NHST is used in articles published in medical journals during recent years, in order to identify what is still lacking in the process of eradicating the widespread ceremonial use that is made of statistics in health research [ 34 ]. Furthermore, it is enlightening in this context to examine whether these patterns differ between English- and Spanish-speaking worlds and, if so, to see if the changes in paradigms are occurring more slowly in Spanish-language publications. In such a case we would offer various suggestions.
In addition to assessing the adherence to the above cited statistical recommendation proposed by ICMJE relative to the use of P-values, we consider it of particular interest to estimate the extent to which the significance fallacy is present, an inertial deficiency that consists of attributing -- explicitly or not -- qualitative importance or practical relevance to the found differences simply because statistical significance was obtained.
Many authors produce misleading statements such as "a significant effect was (or was not) found" when it should be said that "a statistically significant difference was (or was not) found". A detrimental consequence of this equivalence is that some authors believe that finding out whether there is "statistical significance" or not is the aim, so that this term is then mentioned in the conclusions [ 35 ]. This means virtually nothing, except that it indicates that the author is letting a computer do the thinking. Since the real research questions are never statistical ones, the answers cannot be statistical either. Accordingly, the conversion of the dichotomous outcome produced by a NHST into a conclusion is another manifestation of the mentioned fallacy.
The general objective of the present study is to evaluate the extent and quality of use of NHST and CI, both in English- and in Spanish-language biomedical publications, between 1995 and 2006 taking into account the International Committee of Medical Journal Editors recommendations, with particular focus on accuracy regarding interpretation of statistical significance and the validity of conclusions.
We reviewed the original articles from six journals, three in English and three in Spanish, over three disjoint periods sufficiently separated from each other (1995-1996, 2000-2001, 2005-2006) as to properly describe the evolution in prevalence of the target features along the selected periods.
The selection of journals was intended to get representation for each of the following three thematic areas: clinical specialties ( Obstetrics & Gynecology and Revista Española de Cardiología) ; Public Health and Epidemiology ( International Journal of Epidemiology and Atención Primaria) and the area of general and internal medicine ( British Medical Journal and Medicina Clínica ). Five of the selected journals formally endorsed ICMJE guidelines; the remaining one ( Revista Española de Cardiología ) suggests observing ICMJE demands in relation with specific issues. We attempted to capture journal diversity in the sample by selecting general and specialty journals with different degrees of influence, resulting from their impact factors in 2007, which oscillated between 1.337 (MC) and 9.723 (BMJ). No special reasons guided us to choose these specific journals, but we opted for journals with rather large paid circulations. For instance, the Spanish Cardiology Journal is the one with the largest impact factor among the fourteen Spanish Journals devoted to clinical specialties that have impact factor and Obstetrics & Gynecology has an outstanding impact factor among the huge number of journals available for selection.
It was decided to take around 60 papers for each biennium and journal, which means a total of around 1,000 papers. As recently suggested [ 36 , 37 ], this number was not established using a conventional method, but by means of a purposive and pragmatic approach in choosing the maximum sample size that was feasible.
Systematic sampling in phases [ 38 ] was used in applying a sampling fraction equal to 60/N, where N is the number of articles, in each of the 18 subgroups defined by crossing the six journals and the three time periods. Table Table1 1 lists the population size and the sample size for each subgroup. While the sample within each subgroup was selected with equal probability, estimates based on other subsets of articles (defined across time periods, areas, or languages) are based on samples with various selection probabilities. Proper weights were used to take into account the stratified nature of the sampling in these cases.
Sizes of the populations (and the samples) for selected journals and periods.
Clinical | General Medicine | Public Health and Epidemiology | |||||
---|---|---|---|---|---|---|---|
1995-1996 | 623 (62) | 125 (60) | 346 (62) | 238 (61) | 315 (60) | 169 (60) | 1816 (365) |
2000-2001 | 600 (60) | 146 (60) | 519 (62) | 196 (61) | 286 (60) | 145 (61) | 1892 (364) |
2005-2006 | 537 (59) | 144 (59) | 474 (62) | 158 (62) | 212 (61) | 167 (60) | 1692 (363) |
Total | 1760 (181) | 415 (179) | 1339 (186) | 592 (184) | 813 (181) | 481 (181) | 5400 (1092) |
G&O: Obstetrics & Gynecology; REC: Revista Española de Cardiología; BMJ: British Medical Journal; MC: Medicina Clínica; IJE: International Journal of Epidemiology; AP: Atención Primaria .
Forty-nine of the 1,092 selected papers were eliminated because, although the section of the article in which they were assigned could suggest they were originals, detailed scrutiny revealed that in some cases they were not. The sample, therefore, consisted of 1,043 papers. Each of them was classified into one of three categories: (1) purely descriptive papers, those designed to review or characterize the state of affairs as it exists at present, (2) analytical papers, or (3) articles that address theoretical, methodological or conceptual issues. An article was regarded as analytical if it seeks to explain the reasons behind a particular occurrence by discovering causal relationships or, even if self-classified as descriptive, it was carried out to assess cause-effect associations among variables. We classify as theoretical or methodological those articles that do not handle empirical data as such, and focus instead on proposing or assessing research methods. We identified 169 papers as purely descriptive or theoretical, which were therefore excluded from the sample. Figure Figure1 1 presents a flow chart showing the process for determining eligibility for inclusion in the sample.
Flow chart of the selection process for eligible papers .
To estimate the adherence to ICMJE recommendations, we considered whether the papers used P-values, confidence intervals, and both simultaneously. By "the use of P-values" we mean that the article contains at least one P-value, explicitly mentioned in the text or at the bottom of a table, or that it reports that an effect was considered as statistically significant . It was deemed that an article uses CI if it explicitly contained at least one confidence interval, but not when it only provides information that could allow its computation (usually by presenting both the estimate and the standard error). Probability intervals provided in Bayesian analysis were classified as confidence intervals (although conceptually they are not the same) since what is really of interest here is whether or not the authors quantify the findings and present them with appropriate indicators of the margin of error or uncertainty.
In addition we determined whether the "Results" section of each article attributed the status of "significant" to an effect on the sole basis of the outcome of a NHST (i.e., without clarifying that it is strictly statistical significance). Similarly, we examined whether the term "significant" (applied to a test) was mistakenly used as synonymous with substantive , relevant or important . The use of the term "significant effect" when it is only appropriate as a reference to a "statistically significant difference," can be considered a direct expression of the significance fallacy [ 39 ] and, as such, constitutes one way to detect the problem in a specific paper.
We also assessed whether the "Conclusions," which sometimes appear as a separate section in the paper or otherwise in the last paragraphs of the "Discussion" section mentioned statistical significance and, if so, whether any of such mentions were no more than an allusion to results.
To perform these analyses we considered both the abstract and the body of the article. To assess the handling of the significance issue, however, only the body of the manuscript was taken into account.
The information was collected by four trained observers. Every paper was assigned to two reviewers. Disagreements were discussed and, if no agreement was reached, a third reviewer was consulted to break the tie and so moderate the effect of subjectivity in the assessment.
In order to assess the reliability of the criteria used for the evaluation of articles and to effect a convergence of criteria among the reviewers, a pilot study of 20 papers from each of three journals ( Clinical Medicine , Primary Care , and International Journal of Epidemiology) was performed. The results of this pilot study were satisfactory. Our results are reported using percentages together with their corresponding confidence intervals. For sampling errors estimations, used to obtain confidence intervals, we weighted the data using the inverse of the probability of selection of each paper, and we took into account the complex nature of the sample design. These analyses were carried out with EPIDAT [ 40 ], a specialized computer program that is readily available.
A total of 1,043 articles were reviewed, of which 874 (84%) were found to be analytic, while the remainders were purely descriptive or of a theoretical and methodological nature. Five of them did not employ either P-values or CI. Consequently, the analysis was made using the remaining 869 articles.
The percentage of articles that use only P-values, without even mentioning confidence intervals, to report their results has declined steadily throughout the period analyzed (Table (Table2). 2 ). The percentage decreased from approximately 41% in 1995-1996 to 21% in 2005-2006. However, it does not differ notably among journals of different languages, as shown by the estimates and confidence intervals of the respective percentages. Concerning thematic areas, it is highly surprising that most of the clinical articles ignore the recommendations of ICMJE, while for general and internal medicine papers such a problem is only present in one in five papers, and in the area of Public Health and Epidemiology it occurs only in one out of six. The use of CI alone (without P-values) has increased slightly across the studied periods (from 9% to 13%), but it is five times more prevalent in Public Health and Epidemiology journals than in Clinical ones, where it reached a scanty 3%.
Prevalence of NHST and CI across periods, languages and research areas.
Total of papers | P-values and no CI | CI and P-values | CI and no P-values | |||||||
---|---|---|---|---|---|---|---|---|---|---|
n | % (95%CI) | n | % (95%CI) | n | % (95%CI) | |||||
Period | 1995-1996 | 285 | 119 | 41 (35 to 47) | 138 | 49 (43 to 55) | 28 | 10 (6 to13) | ||
2000-2001 | 278 | 101 | 38 (31 to 44) | 150 | 51 (44 to 58) | 27 | 11 (6 to 15) | |||
2005-2006 | 306 | 65 | 21 (16 to 26) | 198 | 65 (59 to 71) | 43 | 14 (9 to 17) | |||
Language | Spanish | 396 | 156 | 39 (34 to 43) | 211 | 54 (49 to 59) | 29 | 7 (5 to 10) | ||
English | 473 | 129 | 32 (28 to 36) | 275 | 55 (51 to 60) | 69 | 12 (10 to 15) | |||
Area | Clinical | 300 | 166 | 52 (45 to 58) | 125 | 45 (39 to 51) | 9 | 3 (1 to 6) | ||
General Medicine | 278 | 69 | 22 (17 to 27) | 170 | 61 (55 to 67) | 39 | 17 (12 to 22) | |||
Public Health and Epidemiology | 291 | 50 | 18 (13 to 23) | 191 | 65 (59 to 71) | 50 | 17 (13 to 22) |
CI: Confidence Interval
While the percentage of articles referring implicitly or explicitly to significance in an ambiguous or incorrect way - that is, incurring the significance fallacy -- seems to decline steadily, the prevalence of this problem exceeds 69%, even in the most recent period. This percentage was almost the same for articles written in Spanish and in English, but it was notably higher in the Clinical journals (81%) compared to the other journals, where the problem occurs in approximately 7 out of 10 papers (Table (Table3). 3 ). The kappa coefficient for measuring agreement between observers concerning the presence of the "significance fallacy" was 0.78 (CI95%: 0.62 to 0.93), which is considered acceptable in the scale of Landis and Koch [ 41 ].
Frequency of occurrence of the significance fallacy across periods, languages and research areas.
Criteria | Categories | Number of papers examined | Frequency of occurrence of the significance fallacy | % (95%CI) |
---|---|---|---|---|
Period | 1995-1996 | 285 | 224 | 80 (75 to 85) |
2000-2001 | 278 | 210 | 78 (72 to 83) | |
2005-2006 | 306 | 216 | 70 (64 to 75) | |
Language | Spanish | 396 | 295 | 73 (69 to 78) |
English | 473 | 355 | 76 (73 to 80) | |
Area | Clinical | 300 | 248 | 81(76 to 86) |
General Medicine | 278 | 200 | 72 (66 to 77) | |
Public Health and Epidemiology | 291 | 202 | 71 (66 to 76) |
The percentage of papers mentioning a numerical finding as a conclusion is similar in the three periods analyzed (Table (Table4). 4 ). Concerning languages, this percentage is nearly twice as large for Spanish journals as for those published in English (approximately 21% versus 12%). And, again, the highest percentage (16%) corresponded to clinical journals.
Frequency of use of numerical results in conclusions across periods, languages and research areas.
Criteria | Categories | Number of papers examined | Frequency of use of numerical results in conclusions | % (95%CI) |
---|---|---|---|---|
Period | 1995-1996 | 285 | 44 | 15 (10 to 19) |
2000-2001 | 278 | 48 | 15 (10 to 20) | |
2005-2006 | 306 | 45 | 12,1 (8 to 16) | |
Language | Spanish | 396 | 85 | 21 (17 to 25) |
English | 473 | 52 | 12 (9 to 15) | |
Area | Clinical | 300 | 58 | 16 (12 to 21) |
General Medicine | 278 | 39 | 13 (9 to 17) | |
Public Health and Epidemiology | 291 | 40 | 12 (8 to 15) |
A similar pattern is observed, although with less pronounced differences, in references to the outcome of the NHST (significant or not) in the conclusions (Table (Table5). 5 ). The percentage of articles that introduce the term in the "Conclusions" does not appreciably differ between articles written in Spanish and in English. Again, the area where this insufficiency is more often present (more than 15% of articles) is the Clinical area.
Frequency of presence of the term Significance (or statistical significance) in conclusions across periods, languages and research areas.
Criteria | Categories | Number of papers examined | Frequency of presence of significance in conclusions | % (95%CI) |
---|---|---|---|---|
Period | 1995-1996 | 285 | 35 | 14 (9 to 19) |
2000-2001 | 278 | 32 | 12 (8 to 16) | |
2005-2006 | 306 | 41 | 14 (9 to 19) | |
Language | Spanish | 396 | 39 | 10 (7 to 13) |
English | 473 | 69 | 15 (11 to 18) | |
Area | Clinical | 300 | 44 | 16 (11 to 20) |
General Medicine | 278 | 30 | 11 (7 to 15) | |
Public Health and Epidemiology | 291 | 34 | 12 (8 to 16) |
There are some previous studies addressing the degree to which researchers have moved beyond the ritualistic use of NHST to assess their hypotheses. This has been examined for areas such as biology [ 42 ], organizational research [ 43 ], or psychology [ 44 - 47 ]. However, to our knowledge, no recent research has explored the pattern of use P-values and CI in medical literature and, in any case, no efforts have been made to study this problem in a way that takes into account different languages and specialties.
At first glance it is puzzling that, after decades of questioning and technical warnings, and after twenty years since the inception of ICMJE recommendation to avoid NHST, they continue being applied ritualistically and mindlessly as the dominant doctrine. Not long ago, when researchers did not observe statistically significant effects, they were unlikely to write them up and to report "negative" findings, since they knew there was a high probability that the paper would be rejected. This has changed a bit: editors are more prone to judge all findings as potentially eloquent. This is probably the frequent denunciations of the tendency for those papers presenting a significant positive result to receive more favorable publication decisions than equally well-conducted ones that report a negative or null result, the so-called publication bias [ 48 - 50 ]. This new openness is consistent with the fact that if the substantive question addressed is really relevant, the answer (whether positive or negative) will also be relevant.
Consequently, even though it was not an aim of our study, we found many examples in which statistical significance was not obtained. However, many of those negative results were reported with a comment of this type: " The results did not show a significant difference between groups; however, with a larger sample size, this difference would have probably proved to be significant ". The problem with this statement is that it is true; more specifically, it will always be true and it is, therefore, sterile. It is not fortuitous that one never encounters the opposite, and equally tautological, statement: " A significant difference between groups has been detected; however, perhaps with a smaller sample size, this difference would have proved to be not significant" . Such a double standard is itself an unequivocal sign of the ritual application of NHST.
Although the declining rates of NHST usage show that, gradually, ICMJE and similar recommendations are having a positive impact, most of the articles in the clinical setting still considered NHST as the final arbiter of the research process. Moreover, it appears that the improvement in the situation is mostly formal, and the percentage of articles that fall into the significance fallacy is huge.
The contradiction between what has been conceptually recommended and the common practice is sensibly less acute in the area of Epidemiology and Public Health, but the same pattern was evident everywhere in the mechanical way of applying significance tests. Nevertheless, the clinical journals remain the most unmoved by the recommendations.
The ICMJE recommendations are not cosmetic statements but substantial ones, and the vigorous exhortations made by outstanding authorities [ 51 ] are not mere intellectual exercises due to ingenious and inopportune methodologists, but rather they are very serious epistemological warnings.
In some cases, the role of CI is not as clearly suitable (e.g. when estimating multiple regression coefficients or because effect sizes are not available for some research designs [ 43 , 52 ]), but when it comes to estimating, for example, an odds ratio or a rates difference, the advantage of using CI instead of P values is very clear, since in such cases it is obvious that the goal is to assess what has been called the "effect size."
The inherent resistance to change old paradigms and practices that have been entrenched for decades is always high. Old habits die hard. The estimates and trends outlined are entirely consistent with Alvan Feinstein's warning 25 years ago: "Because the history of medical research also shows a long tradition of maintaining loyalty to established doctrines long after the doctrines had been discredited, or shown to be valueless, we cannot expect a sudden change in this medical policy merely because it has been denounced by leading connoisseurs of statistics [ 53 ]".
It is possible, however, that the nature of the problem has an external explanation: it is likely that some editors prefer to "avoid troubles" with the authors and vice versa, thus resorting to the most conventional procedures. Many junior researchers believe that it is wise to avoid long back-and-forth discussions with reviewers and editors. In general, researchers who want to appear in print and survive in a publish-or-perish environment are motivated by force, fear, and expedience in their use of NHST [ 54 ]. Furthermore, it is relatively natural that simple researchers use NHST when they take into account that some theoretical objectors have used this statistical analysis in empirical studies, published after the appearance of their own critiques [ 55 ].
For example, Journal of the American Medical Association published a bibliometric study [ 56 ] discussing the impact of statisticians' co-authorship of medical papers on publication decisions by two major high-impact journals: British Medical Journal and Annals of Internal Medicine . The data analysis is characterized by methodological orthodoxy. The authors just use chi-square tests without any reference to CI, although the NHST had been repeatedly criticized over the years by two of the authors:
Douglas Altman, an early promoter of confidence intervals as an alternative [ 57 ], and Steve Goodman, a critic of NHST from a Bayesian perspective [ 58 ]. Individual authors, however, cannot be blamed for broader institutional problems and systemic forces opposed to change.
The present effort is certainly partial in at least two ways: it is limited to only six specific journals and to three biennia. It would be therefore highly desirable to improve it by studying the problem in a more detailed way (especially by reviewing more journals with different profiles), and continuing the review of prevailing patterns and trends.
The authors declare that they have no competing interests.
LCSA designed the study, wrote the paper and supervised the whole process; PSG coordinated the data extraction and carried out statistical analysis, as well as participated in the editing process; AFS extracted the data and participated in the first stage of statistical analysis; all authors contributed to and revised the final manuscript.
The pre-publication history for this paper can be accessed here:
http://www.biomedcentral.com/1471-2288/10/44/prepub
The authors would like to thank Tania Iglesias-Cabo and Vanesa Alvarez-González for their help with the collection of empirical data and their participation in an earlier version of the paper. The manuscript has benefited greatly from thoughtful, constructive feedback by Carlos Campillo-Artero, Tom Piazza and Ann Séror.
The alternative hypothesis.
Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and behavioral finance. Adam received his master's in economics from The New School for Social Research and his Ph.D. from the University of Wisconsin-Madison in sociology. He is a CFA charterholder as well as holding FINRA Series 7, 55 & 63 licenses. He currently researches and teaches economic sociology and the social studies of finance at the Hebrew University in Jerusalem.
A null hypothesis is a type of statistical hypothesis that proposes that no statistical significance exists in a set of given observations. Hypothesis testing is used to assess the credibility of a hypothesis by using sample data. Sometimes referred to simply as the “null,” it is represented as H 0 .
The null hypothesis, also known as “the conjecture,” is used in quantitative analysis to test theories about markets, investing strategies, and economies to decide if an idea is true or false.
Alex Dos Diaz / Investopedia
A gambler may be interested in whether a game of chance is fair. If it is, then the expected earnings per play come to zero for both players. If it is not, then the expected earnings are positive for one player and negative for the other.
To test whether the game is fair, the gambler collects earnings data from many repetitions of the game, calculates the average earnings from these data, then tests the null hypothesis that the expected earnings are not different from zero.
If the average earnings from the sample data are sufficiently far from zero, then the gambler will reject the null hypothesis and conclude the alternative hypothesis—namely, that the expected earnings per play are different from zero. If the average earnings from the sample data are near zero, then the gambler will not reject the null hypothesis, concluding instead that the difference between the average from the data and zero is explainable by chance alone.
A null hypothesis can only be rejected, not proven.
The null hypothesis assumes that any kind of difference between the chosen characteristics that you see in a set of data is due to chance. For example, if the expected earnings for the gambling game are truly equal to zero, then any difference between the average earnings in the data and zero is due to chance.
Analysts look to reject the null hypothesis because doing so is a strong conclusion. This requires evidence in the form of an observed difference that is too large to be explained solely by chance. Failing to reject the null hypothesis—that the results are explainable by chance alone—is a weak conclusion because it allows that while factors other than chance may be at work, they may not be strong enough for the statistical test to detect them.
An important point to note is that we are testing the null hypothesis because there is an element of doubt about its validity. Whatever information that is against the stated null hypothesis is captured in the alternative (alternate) hypothesis (H 1 ).
For the examples below, the alternative hypothesis would be:
In other words, the alternative hypothesis is a direct contradiction of the null hypothesis.
Here is a simple example: A school principal claims that students in her school score an average of seven out of 10 in exams. The null hypothesis is that the population mean is not 7.0. To test this null hypothesis, we record marks of, say, 30 students ( sample ) from the entire student population of the school (say, 300) and calculate the mean of that sample.
We can then compare the (calculated) sample mean to the (hypothesized) population mean of 7.0 and attempt to reject the null hypothesis. (The null hypothesis here—that the population mean is not 7.0—cannot be proved using the sample data. It can only be rejected.)
Take another example: The annual return of a particular mutual fund is claimed to be 8%. Assume that the mutual fund has been in existence for 20 years. The null hypothesis is that the mean return is not 8% for the mutual fund. We take a random sample of annual returns of the mutual fund for, say, five years (sample) and calculate the sample mean. We then compare the (calculated) sample mean to the (claimed) population mean (8%) to test the null hypothesis.
For the above examples, null hypotheses are:
For the purposes of determining whether to reject the null hypothesis (abbreviated H0), said hypothesis is assumed, for the sake of argument, to be true. Then the likely range of possible values of the calculated statistic (e.g., the average score on 30 students’ tests) is determined under this presumption (e.g., the range of plausible averages might range from 6.2 to 7.8 if the population mean is 7.0).
If the sample average is outside of this range, the null hypothesis is rejected. Otherwise, the difference is said to be “explainable by chance alone,” being within the range that is determined by chance alone.
As an example related to financial markets, assume Alice sees that her investment strategy produces higher average returns than simply buying and holding a stock . The null hypothesis states that there is no difference between the two average returns, and Alice is inclined to believe this until she can conclude contradictory results.
Refuting the null hypothesis would require showing statistical significance, which can be found by a variety of tests. The alternative hypothesis would state that the investment strategy has a higher average return than a traditional buy-and-hold strategy.
One tool that can determine the statistical significance of the results is the p-value. A p-value represents the probability that a difference as large or larger than the observed difference between the two average returns could occur solely by chance.
A p-value that is less than or equal to 0.05 often indicates whether there is evidence against the null hypothesis. If Alice conducts one of these tests, such as a test using the normal model, resulting in a significant difference between her returns and the buy-and-hold returns (the p-value is less than or equal to 0.05), she can then reject the null hypothesis and conclude the alternative hypothesis.
The analyst or researcher establishes a null hypothesis based on the research question or problem they are trying to answer. Depending on the question, the null may be identified differently. For example, if the question is simply whether an effect exists (e.g., does X influence Y?), the null hypothesis could be H 0 : X = 0. If the question is instead, is X the same as Y, the H 0 would be X = Y. If it is that the effect of X on Y is positive, H 0 would be X > 0. If the resulting analysis shows an effect that is statistically significantly different from zero, the null can be rejected.
In finance , a null hypothesis is used in quantitative analysis. It tests the premise of an investing strategy, the markets, or an economy to determine if it is true or false.
For instance, an analyst may want to see if two stocks, ABC and XYZ, are closely correlated. The null hypothesis would be ABC ≠ XYZ.
Statistical hypotheses are tested by a four-step process . The first is for the analyst to state the two hypotheses so that only one can be right. The second is to formulate an analysis plan, which outlines how the data will be evaluated. The third is to carry out the plan and physically analyze the sample data. The fourth and final step is to analyze the results and either reject the null hypothesis or claim that the observed differences are explainable by chance alone.
An alternative hypothesis is a direct contradiction of a null hypothesis. This means that if one of the two hypotheses is true, the other is false.
A null hypothesis states there is no difference between groups or relationship between variables. It is a type of statistical hypothesis and proposes that no statistical significance exists in a set of given observations. “Null” means nothing.
The null hypothesis is used in quantitative analysis to test theories about economies, investing strategies, and markets to decide if an idea is true or false. Hypothesis testing assesses the credibility of a hypothesis by using sample data. It is represented as H 0 and is sometimes simply known as “the null.”
Sage Publishing. “ Chapter 8: Introduction to Hypothesis Testing ,” Page 4.
Sage Publishing. “ Chapter 8: Introduction to Hypothesis Testing ,” Pages 4 to 7.
Sage Publishing. “ Chapter 8: Introduction to Hypothesis Testing ,” Page 7.
You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .
Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.
Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.
Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.
Original Submission Date Received: .
Find support for a specific problem in the support section of our website.
Please let us know what you think of our products and services.
Visit our dedicated information section to learn more about MDPI.
Unveiling the power of nation branding: exploring the impact of economic factors on global image perception.
2. literature review, 3. theoretical framework of nation branding, 3.1. definition and components of nation branding.
3.3. socioeconomic impacts of nation branding, 3.3.1. human capital and nation branding, 3.3.2. fdi and nation branding, 3.3.3. export and nation branding, 3.3.4. tourism and nation branding, 4. model, data, and methodology, 4.1. model and data, 4.2. cross-sectional dependence test, 5. discussion and conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.
Click here to enlarge figure
Abbreviations | Variables | Measurement | Sources |
---|---|---|---|
lnbrand | Country branding | Country branding index | Brand Finance |
lnfdi | Foreign Direct Investment | Foreign direct investment %GDP | World Bank Development Indicator |
lnexport | Export Value | Export Value Index (2000 = 100) | World Bank Development Indicator |
hdi | Human Development İndex | Human Development İndex | Human Development Reports |
lntourism | International Tourism Expenditures | International Tourism Expenditures (Current US$) | World Bank Development Indicator |
Test Statistics | p-Value | |
---|---|---|
340.7003 * | 0.0000 | |
30.11546 * | 0.0000 | |
18.27894 * | 0.0000 | |
Test statistics | p-value | |
3.390 * | 0.000 | |
5.028 * | 0.000 |
Variable | Constant Level | Constant First Difference |
---|---|---|
Lnbrand | −1.876 ** | −2.695 * |
Lntourism | −1.269 | −1.787 * |
Lnexport | −1.886 ** | −3.307 * |
Hdi | −0.707 | −3.232 * |
Lnfdi | −1.397 | −3.144 * |
Arellano–Bond | Arellano–Bover | |||||
---|---|---|---|---|---|---|
Coefficient | Standard Error | p-Value | Coefficient | Standard Error | p-Value | |
lnbrand (−1) | 0.650675 | 0.115646 | 0.000 | 0.80266 | 0.03901 | 0.000 |
lntourism | 0.0487356 | 0.048735 | 0.081 | 0.12672 | 0.03867 | 0.001 |
lnexport | 0.338162 | 0.200094 | 0.091 | 0.30937 | 0.14229 | 0.030 |
hdi | 2.52831 | 1.35090 | 0.061 | 0.75044 | 0.38299 | 0.050 |
lnfdi | 0.012695 | 0.006422 | 0.048 | 0.01205 | 0.0064 | 0.064 |
Sargan test Statistics | 63.342 (0.0356) | 65.58509 (0.1149) | ||||
AR (1) | −2.047 (0.042) | −2.0294 (0.042) | ||||
AR (2) | −0.72489 (0.468) | −0.74952 (0.4535) | ||||
Number of Observations | 270 | |||||
Number of Groups | 10 |
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
Dineri, E.; Bilginer Özsaatcı, F.G.; Kılıç, Y.; Çiğdem, Ş.; Sayar, G. Unveiling the Power of Nation Branding: Exploring the Impact of Economic Factors on Global Image Perception. Sustainability 2024 , 16 , 6950. https://doi.org/10.3390/su16166950
Dineri E, Bilginer Özsaatcı FG, Kılıç Y, Çiğdem Ş, Sayar G. Unveiling the Power of Nation Branding: Exploring the Impact of Economic Factors on Global Image Perception. Sustainability . 2024; 16(16):6950. https://doi.org/10.3390/su16166950
Dineri, Eda, Fatma Gül Bilginer Özsaatcı, Yunus Kılıç, Şemsettin Çiğdem, and Gökçen Sayar. 2024. "Unveiling the Power of Nation Branding: Exploring the Impact of Economic Factors on Global Image Perception" Sustainability 16, no. 16: 6950. https://doi.org/10.3390/su16166950
Further information, mdpi initiatives, follow mdpi.
Subscribe to receive issue release notifications and newsletters from MDPI journals
IMAGES
COMMENTS
The null and alternative hypotheses offer competing answers to your research question. When the research question asks "Does the independent variable affect the dependent variable?": The null hypothesis ( H0) answers "No, there's no effect in the population.". The alternative hypothesis ( Ha) answers "Yes, there is an effect in the ...
The null hypothesis in statistics states that there is no difference between groups or no relationship between variables. It is one of two mutually exclusive hypotheses about a population in a hypothesis test. When your sample contains sufficient evidence, you can reject the null and conclude that the effect is statistically significant.
The null hypothesis and the alternative hypothesis are types of conjectures used in statistical tests to make statistical inferences, which are formal methods of reaching conclusions and separating scientific claims from statistical noise.. The statement being tested in a test of statistical significance is called the null hypothesis. The test of significance is designed to assess the strength ...
HYPOTHESIS TESTING. A clinical trial begins with an assumption or belief, and then proceeds to either prove or disprove this assumption. In statistical terms, this belief or assumption is known as a hypothesis. Counterintuitively, what the researcher believes in (or is trying to prove) is called the "alternate" hypothesis, and the opposite ...
The null and alternative hypotheses are two competing claims that researchers weigh evidence for and against using a statistical test: Null hypothesis (H0): There's no effect in the population. Alternative hypothesis (HA): There's an effect in the population. The effect is usually the effect of the independent variable on the dependent ...
It is the opposite of your research hypothesis. The alternative hypothesis--that is, the research hypothesis--is the idea, phenomenon, observation that you want to prove. If you suspect that girls take longer to get ready for school than boys, then: Alternative: girls time > boys time. Null: girls time <= boys time.
Null Hypothesis Examples. "Hyperactivity is unrelated to eating sugar " is an example of a null hypothesis. If the hypothesis is tested and found to be false, using statistics, then a connection between hyperactivity and sugar ingestion may be indicated. A significance test is the most common statistical test used to establish confidence in a ...
Step 1: Figure out the hypothesis from the problem. The hypothesis is usually hidden in a word problem, and is sometimes a statement of what you expect to happen in the experiment. The hypothesis in the above question is "I expect the average recovery period to be greater than 8.2 weeks.". Step 2: Convert the hypothesis to math.
Review. In a hypothesis test, sample data is evaluated in order to arrive at a decision about some type of claim.If certain conditions about the sample are satisfied, then the claim can be evaluated for a population. In a hypothesis test, we: Evaluate the null hypothesis, typically denoted with \(H_{0}\).The null is not rejected unless the hypothesis test shows otherwise.
The actual test begins by considering two hypotheses.They are called the null hypothesis and the alternative hypothesis.These hypotheses contain opposing viewpoints. H 0, the —null hypothesis: a statement of no difference between sample means or proportions or no difference between a sample mean or proportion and a population mean or proportion. In other words, the difference equals 0.
A null hypothesis is a statistical concept suggesting no significant difference or relationship between measured variables. It's the default assumption unless empirical evidence proves otherwise. The null hypothesis states no relationship exists between the two variables being studied (i.e., one variable does not affect the other).
Biology definition: A null hypothesis is an assumption or proposition where an observed difference between two samples of a statistical population is purely accidental and not due to systematic causes. It is the hypothesis to be investigated through statistical hypothesis testing so that when refuted indicates that the alternative hypothesis is true. . Thus, a null hypothesis is a hypothesis ...
H0: µ = 75. H0: µ = µ0. Ha: There will be a statistically significant difference between the student's score and the class average score on the math exam. Ha: µ ≠ 75. Ha: µ ≠ µ0. In the null hypothesis, there is no difference between the observed mean (µ) and the claimed value (75). However, in the alternative hypothesis, class ...
The actual test begins by considering two hypotheses.They are called the null hypothesis and the alternative hypothesis.These hypotheses contain opposing viewpoints. H 0: The null hypothesis: It is a statement about the population that either is believed to be true or is used to put forth an argument unless it can be shown to be incorrect beyond a reasonable doubt.
H 0 (Null Hypothesis): Population parameter =, ≤, ≥ some value. H A (Alternative Hypothesis): Population parameter <, >, ≠ some value. Note that the null hypothesis always contains the equal sign. We interpret the hypotheses as follows: Null hypothesis: The sample data provides no evidence to support some claim being made by an individual.
To distinguish it from other hypotheses, the null hypothesis is written as H 0 (which is read as "H-nought," "H-null," or "H-zero"). A significance test is used to determine the likelihood that the results supporting the null hypothesis are not due to chance. A confidence level of 95% or 99% is common. Keep in mind, even if the confidence level is high, there is still a small chance the ...
This null hypothesis can be written as: H0: X¯ = μ H 0: X ¯ = μ. For most of this textbook, the null hypothesis is that the means of the two groups are similar. Much later, the null hypothesis will be that there is no relationship between the two groups. Either way, remember that a null hypothesis is always saying that nothing is different.
The null hypothesis statistical testing (NHST) has been the most widely used statistical approach in health research over the past 80 years. Its origins dates back to 1279 [] although it was in the second decade of the twentieth century when the statistician Ronald Fisher formally introduced the concept of "null hypothesis" H 0 - which, generally speaking, establishes that certain parameters ...
Assessing statistical significance by means of contrasting the data with the null hypothesis is called Null Hypothesis Significance Testing (NHST). NHST is the best known and most widely used statistical procedure for making inferences about population effects. The procedure has become the prevailing paradigm in empirical science [ 3 ], and ...
The meaning of NULL HYPOTHESIS is a statistical hypothesis to be tested and accepted or rejected in favor of an alternative; specifically : the hypothesis that an observed difference (as between the means of two samples) is due to chance alone and not due to a systematic cause.
NULL HYPOTHESIS. the statement postulating an experiment will find no variations between the control and experimental states, which is, no union between variants. Statistical tests are rendered to experimental outcomes in effort to disprove or refute the previously established significance level.
The null hypothesis significance test (NHST) is the most frequently used statistical method, although its inferential validity has been widely criticized since its introduction. In 1988, the International Committee of Medical Journal Editors (ICMJE) warned against sole reliance on NHST to substantiate study conclusions and suggested ...
Null Hypothesis: A null hypothesis is a type of hypothesis used in statistics that proposes that no statistical significance exists in a set of given observations. The null hypothesis attempts to ...
At a briefing in April 2021, Dr. Theresa Tam, the country's chief public-health officer, advised the working group to engage the widest expert network possible and ensured that the investigation ...
The null hypothesis is defined as homogeneous slopes, while the alternative hypothesis is defined as heterogeneous slopes. According to the results in Table 2 , the H o hypothesis, which claims that there is no cross-sectional dependence, is strongly rejected, and the alternative hypothesis (H 1 ) is accepted.