Null Hypothesis Significance Testing: a short tutorial

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.

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 ◽

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.

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 ◽

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.

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 ◽

Reporting Practices ◽

Interpretation Errors ◽

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.

Null hypothesis significance testing: a short tutorial

F1000Research ◽

10.12688/f1000research.6963.2 ◽

2016 ◽

Vol 4 ◽

pp. 621 ◽

Cited By ~ 2

Author(s):

Cyril Pernet

Keyword(s):

Social Sciences ◽

Statistical Method ◽

Confidence Intervals ◽

Null Hypothesis ◽

Significance Testing ◽

P Value ◽

Reporting Practices ◽

Interpretation Errors ◽

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.

Null hypothesis significance testing: a short tutorial

F1000Research ◽

10.12688/f1000research.6963.3 ◽

2016 ◽

Vol 4 ◽

pp. 621 ◽

Cited By ~ 1

Author(s):

Cyril Pernet

Keyword(s):

Social Sciences ◽

Statistical Method ◽

Confidence Intervals ◽

Null Hypothesis ◽

Significance Testing ◽

P Value ◽

Reporting Practices ◽

Interpretation Errors ◽

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.

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 ◽

Reporting Practices ◽

Interpretation Errors ◽

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.

Null Hypothesis Significance Testing: Ramifications, Ruminations and Recommendations

South African Journal of Psychology ◽

10.1177/008124630503500101 ◽

2005 ◽

Vol 35 (1) ◽

pp. 1-20 ◽

Cited By ~ 2

Author(s):

G. K. Huysamen

Keyword(s):

Sample Size ◽

Confidence Intervals ◽

Effect Size ◽

Null Hypothesis ◽

Significance Testing ◽

Population Parameter ◽

Size Estimation ◽

Point Estimates ◽

Size Estimates

Criticisms of traditional null hypothesis significance testing (NHST) became more pronounced during the 1960s and reached a climax during the past decade. Among others, NHST says nothing about the size of the population parameter of interest and its result is influenced by sample size. Estimation of confidence intervals around point estimates of the relevant parameters, model fitting and Bayesian statistics represent some major departures from conventional NHST. Testing non-nil null hypotheses, determining optimal sample size to uncover only substantively meaningful effect sizes and reporting effect-size estimates may be regarded as minor extensions of NHST. Although there seems to be growing support for the estimation of confidence intervals around point estimates of the relevant parameters, it is unlikely that NHST-based procedures will disappear in the near future. In the meantime, it is widely accepted that effect-size estimates should be reported as a mandatory adjunct to conventional NHST results.

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 ◽

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.

Confidence Intervals

Zeitschrift für Psychologie / Journal of Psychology ◽

10.1027/0044-3409.217.1.15 ◽

2009 ◽

Vol 217 (1) ◽

pp. 15-26 ◽

Cited By ~ 43

Author(s):

Geoff Cumming ◽

Fiona Fidler

Keyword(s):

Confidence Interval ◽

Confidence Intervals ◽

Null Hypothesis ◽

Prediction Intervals ◽

Effect Sizes ◽

Significance Testing ◽

P Values ◽

Standardized Effect Sizes ◽

Mainstream Science

Most questions across science call for quantitative answers, ideally, a single best estimate plus information about the precision of that estimate. A confidence interval (CI) expresses both efficiently. Early experimental psychologists sought quantitative answers, but for the last half century psychology has been dominated by the nonquantitative, dichotomous thinking of null hypothesis significance testing (NHST). The authors argue that psychology should rejoin mainstream science by asking better questions – those that demand quantitative answers – and using CIs to answer them. They explain CIs and a range of ways to think about them and use them to interpret data, especially by considering CIs as prediction intervals, which provide information about replication. They explain how to calculate CIs on means, proportions, correlations, and standardized effect sizes, and illustrate symmetric and asymmetric CIs. They also argue that information provided by CIs is more useful than that provided by p values, or by values of Killeen’s prep, the probability of replication.