In praise of the null hypothesis statistical test.

@article{Hagen1997InPO,
  title={In praise of the null hypothesis statistical test.},
  author={Richard L. Hagen},
  journal={American Psychologist},
  year={1997},
  volume={52},
  pages={15-24}
}
  • R. L. Hagen
  • Published 1997
  • Psychology
  • American Psychologist
Jacob Cohen (1994) raised a number of questions about the logic and information value of the null hypothesis statistical test (NHST). Specifically, he suggested that: (a) The NHST does not tell us what we want to know; (b) the null hypothesis is always false; and (c) the NHST lacks logical integrity. It is the author's view that although there may be good reasons to give up the NHST, these particular points made by Cohen are not among those reasons. When addressing these points, the author also… 

Figures from this paper

Null hypothesis significance testing. On the survival of a flawed method.
TLDR
The criticisms of NHST are reviewed and it is shown that the criticisms address the logical validity of inferences arising from NHST, whereas the defenses stress the pragmatic value of these inferences.
Null hypothesis significance testing: a review of an old and continuing controversy.
TLDR
The concluding opinion is that NHST is easily misunderstood and misused but that when applied with good judgment it can be an effective aid to the interpretation of experimental data.
The Controversy over Null Hypothesis Significance Testing Revisited
Abstract. Null hypothesis significance testing (NHST) is one of the most widely used methods for testing hypotheses in psychological research. However, it has remained shrouded in controversy
A Critical Assessment of Null Hypothesis Significance Testing in Quantitative Communication Research
Null hypothesis significance testing (NHST) is the most widely accepted and frequently used approach to statistical inference in quantitative communication research. NHST, however, is highly
Calculating the main alternatives to null-hypothesis-significance testing in between-subject experimental designs.
TLDR
An attempt is made to provide the applied researcher with resources that make it possible to analyse and interpret the results of any research study using a group of indicators that lends a high level of validity to the statistical inference performed.
Calculating the main alternatives to null-hypothesis-significance testing in between-subject experimental designs.
TLDR
An attempt is made to provide the applied researcher with resources that make it possible to analyse and interpret the results of any research study using a group of indicators that lends a high level of validity to the statistical inference performed.
The proof of the pudding: an illustration of the relative strengths of null hypothesis, meta-analysis, and Bayesian analysis.
TLDR
The authors illustrate the use of NHST along with 2 possible alternatives (meta-analysis as a primary data analysis strategy and Bayesian approaches) in a series of 3 studies to demonstrate that the approaches are not mutually exclusive but instead can be used to complement one another.
A Test of the Null Hypothesis Significance Testing Procedure Correlation Argument
TLDR
The present findings indicate that the correlation is unimpressive and fails to provide a compelling justification for computing p values and as the significance rule becomes more stringent, the correlation decreases.
Statistical Significance and Replicability
Commentators agree with me that statistical significance does not betoken replicability, but not for the reasons I give. Chow (1998) defends the use of significance tests by arguing that they are
The need for Bayesian hypothesis testing in psychological science
This chapter explains why the logic behind p‐value significance tests is faulty, leading researchers to mistakenly believe that their results are diagnostic when they are not. It outlines a Bayesian
...
...

References

SHOWING 1-10 OF 32 REFERENCES
What Statistical Significance Testing Is, and What It Is Not
AbstractA test of statistical significance addresses the question, How likely is a result, assuming the null hypotheses to be true. Randomness, a central assumption underlying commonly used tests of
The fallacy of the null-hypothesis significance test.
TLDR
To the experimental scientist, statistical inference is a research instrument, a processing device by which unwieldy masses of raw data may be refined into a product more suitable for assimilation into the corpus of science, and in this lies both strength and weakness.
The Case Against Statistical Significance Testing, Revisited
AbstractAt present, too many research results in education are blatantly described as significant, when they are in fact trivially small and unimportant. There are several things researchers can do
The Design of Experiments
  • J. I
  • Economics
    Nature
  • 1936
AbstractREADERS of “Statistical Methods for Research Workers” will welcome Prof. Fisher's new book, which is partly devoted to a development of the logical ideas underlying the earlier volume and
The earth is round (p < .05)
After 4 decades of severe criticism, the ritual of null hypothesis significance testing (mechanical dichotomous decisions around a sacred .05 criterion) still persists. This article reviews the
Theoretical risks and tabular asterisks: Sir Karl, Sir Ronald, and the slow progress of soft psychology.
Abstract Theories in “soft” areas of psychology lack the cumulative character of scientific knowledge. They tend neither to be refuted nor corroborated, but instead merely fade away as people lose
Probability and Scientific Method
I argued in my previous paper that the opinions (1) that all inference beyond the immediate data of experience is meaningless, and (2) that the whole of scientific knowledge can be established
Confirmation, Disconfirmation, and Informa-tion in Hypothesis Testing
Strategies for hypothesis testing in scientific investigation and everyday reasoning have interested both psychologists and philosophers. A number of these scholars stress the importance of
The Use of Statistical Significance Tests in Research: Bootstrap and Other Alternatives
AbstractThree of the various criticisms of conventional uses of statistical significance testing are elaborated. Three alternatives for augmenting statistical significance tests in interpreting
Evaluating Results Using Corrected and Uncorrected Effect Size Estimates
AbstractMagnitude-of-effect (ME) statistics, when adequately understood and correctly used, are important aids for researchers who do not want to place a sole reliance on tests of statistical
...
...