Null hypothesis significance testing: a short tutorial

Although thoroughly criticized, null hypothesis significance testing (NHST) remains the statistical method of choice used to provide evidence for an effect, in biological, biomedical and social sciences. In this short tutorial, I first summarize the concepts behind the method, distinguishing test of significance (Fisher) and test of acceptance (Newman-Pearson) and point to common interpretation errors regarding the p-value. I then present the related concepts of confidence intervals and again point to common interpretation errors. Finally, I discuss what should be reported in which context. The goal is to clarify concepts to avoid interpretation errors and propose reporting practices.

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Null hypothesis significance testing: a guide to commonly misunderstood concepts and recommendations for good practice

F1000Research ◽

10.12688/f1000research.6963.5 ◽

2017 ◽

Vol 4 ◽

pp. 621

Author(s):

Cyril Pernet

Keyword(s):

Social Sciences ◽

Confidence Intervals ◽

Null Hypothesis ◽

Good Practice ◽

Significance Testing ◽

P Value ◽

Null Hypothesis Significance Testing ◽

Reporting Practices ◽

Interpretation Errors ◽

Test Of Significance

Although thoroughly criticized, null hypothesis significance testing (NHST) remains the statistical method of choice used to provide evidence for an effect, in biological, biomedical and social sciences. In this short guide, I first summarize the concepts behind the method, distinguishing test of significance (Fisher) and test of acceptance (Newman-Pearson) and point to common interpretation errors regarding the p-value. I then present the related concepts of confidence intervals and again point to common interpretation errors. Finally, I discuss what should be reported in which context. The goal is to clarify concepts to avoid interpretation errors and propose simple reporting practices.

Download Full-text

Null hypothesis significance testing: a guide to commonly misunderstood concepts and recommendations for good practice

F1000Research ◽

10.12688/f1000research.6963.4 ◽

2017 ◽

Vol 4 ◽

pp. 621 ◽

Cited By ~ 5

Author(s):

Cyril Pernet

Keyword(s):

Social Sciences ◽

Confidence Intervals ◽

Null Hypothesis ◽

Good Practice ◽

Significance Testing ◽

P Value ◽

Null Hypothesis Significance Testing ◽

Reporting Practices ◽

Interpretation Errors ◽

Test Of Significance

Although thoroughly criticized, null hypothesis significance testing (NHST) remains the statistical method of choice used to provide evidence for an effect, in biological, biomedical and social sciences. In this short guide, I first summarize the concepts behind the method, distinguishing test of significance (Fisher) and test of acceptance (Newman-Pearson) and point to common interpretation errors regarding the p-value. I then present the related concepts of confidence intervals and again point to common interpretation errors. Finally, I discuss what should be reported in which context. The goal is to clarify concepts to avoid interpretation errors and propose simple reporting practices.

Download Full-text

Null hypothesis significance testing: a short tutorial

F1000Research ◽

10.12688/f1000research.6963.1 ◽

2015 ◽

Vol 4 ◽

pp. 621 ◽

Cited By ~ 3

Author(s):

Cyril Pernet

Keyword(s):

Social Sciences ◽

Statistical Method ◽

Confidence Intervals ◽

Null Hypothesis ◽

Significance Testing ◽

Null Hypothesis Significance Testing ◽

Reporting Practices ◽

Interpretation Errors ◽

Statistical Issues

Although thoroughly criticized, null hypothesis significance testing (NHST) is the statistical method of choice in biological, biomedical and social sciences to investigate if an effect is likely. In this short tutorial, I first summarize the concepts behind the method while pointing to common interpretation errors. I then present the related concepts of confidence intervals, and discuss what should be reported in which context. The goal is to clarify concepts, present statistical issues that researchers face using the NHST framework and propose reporting practices.

Download Full-text

Null Hypothesis Significance Testing: a short tutorial

10.7287/peerj.preprints.1050 ◽

2015 ◽

Author(s):

Cyril R Pernet

Keyword(s):

Social Sciences ◽

Confidence Intervals ◽

Effect Size ◽

Null Hypothesis ◽

Significance Testing ◽

Null Hypothesis Significance Testing ◽

Good Practices ◽

Interpretation Errors ◽

Statistical Issues ◽

Bayesian Factor

Although thoroughly criticized, null hypothesis significance testing is the statistical method of choice in biological, biomedical and social sciences to investigate if an effect is likely. In this short tutorial, I first summarize the concepts behind the method while pointing to common interpretation errors. I then present the related concepts of confidence intervals, effect size, and Bayesian factor, and discuss what should be reported in which context. The goal is to clarify concepts, present statistical issues that researchers face using the NHST framework and highlight good practices.

Download Full-text

Null Hypothesis Significance Testing: a short tutorial

10.7287/peerj.preprints.1050v1 ◽

2015 ◽

Author(s):

Cyril R Pernet

Keyword(s):

Social Sciences ◽

Confidence Intervals ◽

Effect Size ◽

Null Hypothesis ◽

Significance Testing ◽

Null Hypothesis Significance Testing ◽

Good Practices ◽

Interpretation Errors ◽

Statistical Issues ◽

Bayesian Factor

Although thoroughly criticized, null hypothesis significance testing is the statistical method of choice in biological, biomedical and social sciences to investigate if an effect is likely. In this short tutorial, I first summarize the concepts behind the method while pointing to common interpretation errors. I then present the related concepts of confidence intervals, effect size, and Bayesian factor, and discuss what should be reported in which context. The goal is to clarify concepts, present statistical issues that researchers face using the NHST framework and highlight good practices.

Download Full-text

Null Hypothesis Significance Testing: a short tutorial

10.7287/peerj.preprints.1050v2 ◽

2015 ◽

Author(s):

Cyril R Pernet

Keyword(s):

Social Sciences ◽

Confidence Intervals ◽

Effect Size ◽

Null Hypothesis ◽

Significance Testing ◽

Null Hypothesis Significance Testing ◽

Good Practices ◽

Interpretation Errors ◽

Statistical Issues ◽

Bayesian Factor

Although thoroughly criticized, null hypothesis significance testing is the statistical method of choice in biological, biomedical and social sciences to investigate if an effect is likely. In this short tutorial, I first summarize the concepts behind the method while pointing to common interpretation errors. I then present the related concepts of confidence intervals, effect size, and Bayesian factor, and discuss what should be reported in which context. The goal is to clarify concepts, present statistical issues that researchers face using the NHST framework and highlight good practices.

Download Full-text

Distinguishing Between Models and Hypotheses: Implications for Significance Testing

10.31234/osf.io/759qy ◽

2021 ◽

Author(s):

David Trafimow

Keyword(s):

Social Sciences ◽

Null Hypothesis ◽

Significance Testing ◽

P Value ◽

Null Hypothesis Significance Testing ◽

Test Hypothesis

In the debate about the merits or demerits of null hypothesis significance testing (NHST), authorities on both sides assume that the p value that a researcher computes is based on the null hypothesis or test hypothesis. If the assumption is true, it suggests that there are proper uses for NHST, such as distinguishing between competing directional hypotheses. And once it is admitted that there are proper uses for NHST, it makes sense to educate substantive researchers about how to use NHST properly and avoid using it improperly. From this perspective, the conclusion would be that researchers in the business and social sciences could benefit from better education pertaining to NHST. In contrast, my goal is to demonstrate that the p value that a researcher computes is not based on a hypothesis, but on a model in which the hypothesis is embedded. In turn, the distinction between hypotheses and models indicates that NHST cannot soundly be used to distinguish between competing directional hypotheses or to draw any conclusions about directional hypotheses whatsoever. Therefore, it is not clear that better education is likely to prove satisfactory. It is the temptation issue, not the education issue, that deserves to be in the forefront of NHST discussions.

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A Frequentist Alternative to Significance Testing, p-Values, and Confidence Intervals

Econometrics ◽

10.3390/econometrics7020026 ◽

2019 ◽

Vol 7 (2) ◽

pp. 26 ◽

Cited By ~ 7

Author(s):

David Trafimow

Keyword(s):

Present Article ◽

Confidence Intervals ◽

Null Hypothesis ◽

A Priori ◽

Significance Testing ◽

Population Parameters ◽

Null Hypothesis Significance Testing ◽

P Values ◽

Statistical Procedures ◽

Major Section

There has been much debate about null hypothesis significance testing, p-values without null hypothesis significance testing, and confidence intervals. The first major section of the present article addresses some of the main reasons these procedures are problematic. The conclusion is that none of them are satisfactory. However, there is a new procedure, termed the a priori procedure (APP), that validly aids researchers in obtaining sample statistics that have acceptable probabilities of being close to their corresponding population parameters. The second major section provides a description and review of APP advances. Not only does the APP avoid the problems that plague other inferential statistical procedures, but it is easy to perform too. Although the APP can be performed in conjunction with other procedures, the present recommendation is that it be used alone.

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Роль показника розміру ефекту в сучасних психологічних дослідженнях

10.52363/dcpp-2021.2.9 ◽

2021 ◽

Author(s):

Валерій Боснюк

Keyword(s):

Null Hypothesis ◽

Significance Testing ◽

P Value ◽

Null Hypothesis Significance Testing

Для підтвердження результатів дослідження в психологічних наукових роботах протягом багатьох років використовується процедура перевірки значущості нульової гіпотези (загальноприйнята абревіатура NHST – Null Hypothesis Significance Testing) із застосуванням спеціальних статистичних критеріїв. При цьому здебільшого значення статистики «p» (p-value) розглядається як еквівалент важливості отриманих результатів і сили наукових доказів на користь практичного й теоретичного ефекту дослідження. Таке некоректне використання та інтерпретації p-value ставить під сумнів застосування статистики взагалі та загрожує розвитку психології як науки. Ототожнення статистичного висновку з науковим висновком, орієнтація виключно на новизну в наукових дослідженнях, ритуальна прихильність дослідників до рівня значущості 0,05, опора на статистичну категоричність «так/ні» під час прийняття рішення призводить до того, що психологія примножує тільки результати про наявність ефекту без врахування його величини, практичної цінності. Дана робота призначена для аналізу обмеженості p-value при інтерпретації результатів психологічних досліджень та переваг представлення інформації про розмір ефекту. Застосування розмірів ефекту дозволить здійснити перехід від дихотомічного мислення до оціночного, визначати цінність результатів незалежно від рівня статистичної значущості, приймати рішення більш раціонально та обґрунтовано. Обґрунтовується позиція, що автор наукової роботи при формулюванні висновків дослідження не повинен обмежуватися одним єдиним показником рівня статистичної значущості. Осмислені висновки повинні базуватися на розумному балансуванні p-value та інших не менш важливих параметрів, одним з яких виступає розмір ефекту. Ефект (відмінність, зв’язок, асоціація) може бути статистично значущим, а його практична (клінічна) цінність – незначною, тривіальною. «Статистично значущий» не означає «корисний», «важливий», «цінний», «значний». Тому звернення уваги психологів до питання аналізу виявленого розміру ефекту має стати обов’язковим при інтерпретації результатів дослідження.

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