Results 201 to 210 of about 113,606 (254)

Complementing the P-value from null-hypothesis significance testing with a Bayes factor from null-hypothesis Bayesian testing

Nurse Researcher, 2020
Classical frequentist statistics, including null-hypothesis significance testing (NHST), dominates nursing and medical research analysis. However, there is increasing recognition that null-hypothesis Bayesian testing (NHBT) merits inclusion in healthcare research analysis.To recommend that researchers complement the P-value from NHST with a Bayes ...
Helen Evelyn, Malone, Imelda, Coyne
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Null Hypothesis Significance Testing: Effect Size Matters

Human Dimensions of Wildlife, 2001
A statistically significant outcome only indicates that it is likely that there is a relationship between variables. It does not describe the extent (strength) of that relationship. In this article, emphasis is placed on the importance of assessing the strength of the relationship between the independent and dependent variables using effect size ...
Jeffrey A. Gliner, Jerry J. Vaske, George A. Morgan   +2 more
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A Test of the Null Hypothesis Significance Testing Procedure Correlation Argument

The Journal of General Psychology, 2009
Some supporters of the null hypothesis significance testing procedure recognize that the logic on which it depends is invalid because it only produces the probability of the data if given the null hypothesis and not the probability of the null hypothesis if given the data (e.g., J. Krueger, 2001).
David, Trafimow, Stephen, Rice
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The historical case against null-hypothesis significance testing

Behavioral and Brain Sciences, 1998
We argue that Chow's defense of hypothesis-testing procedures attempts to restore an aura of objectivity to the core procedures, allowing these to take on the role of judgment that should be reserved for the researcher. We provide a brief overview of what we call the historical case against hypothesis testing and argue that the latter has led to a
Henderikus J. Stam, Grant A. Pasay
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Overview of null hypothesis significance testing

2019
This chapter assesses the concepts and procedures common to all statistical tests within null hypothesis significance testing (NHST). In brief, NHST is a procedure used to make a decision about whether chance alone can account for apparent patterns in the data.
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The Insignificance of Null Hypothesis Significance Testing

Political Research Quarterly, 1999
The current method of hypothesis testing in the social sciences is under intense criticism, yet most political scientists are unaware of the important issues being raised. Criticisms focus on the construction and interpretation of a procedure that has dominated the reporting of empirical results for over fifty years.
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Null Hypothesis Statistical Significance Testing and z-Tests

2018
In the previous chapter, we learned about the theory of statistical inference. This theory provides statisticians, researchers, and students with the background information they need to make inferences about a population based on sample data. This chapter builds upon that theoretical foundation by teaching about the simplest possible inferential ...
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Null Hypothesis Significance Testing: Ramifications, Ruminations and Recommendations

South African Journal of Psychology, 2005
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.
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Malignant side effects of null-hypothesis significance testing

Theory & Psychology, 2014
Six decades-worth of published information has shown irrefutably that null-hypothesis significance tests (NHSTs) provide no information about the reliability of research outcomes. Nevertheless, they are still the core of editorial decision-making in Psychology. Two reasons appear to contribute to the continuing practice.
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Null Hypothesis Significance Testing Does Not Show Equivalence

Analyses of Social Issues and Public Policy, 2015
Null hypothesis significance testing (NHST) allows researchers to find differences between groups, such as differences between men and women, Blacks and Whites, or heterosexual and same‐sex couples. In contrast, this article will show that NHST is mathematically incapable of allowing researchers to conclude that two groups are the same. Because of this,
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