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Related Concept Videos

Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

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When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
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Decision Making: P-value Method01:09

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Decision Making: Traditional Method01:14

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
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Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

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The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the...
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Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

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Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
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Statistical Hypothesis Testing01:16

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Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
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Updated: Nov 21, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Bayesian Data Analysis: A Fresh Approach to Power Issues and Null Hypothesis Interpretation.

J Peter Rosenfeld1, Joseph M Olson2

  • 1Department of Psychology and Institute for Neuroscience, Northwestern University, Evanston, IL, USA. jp-rosenfeld@northwestern.edu.

Applied Psychophysiology and Biofeedback
|January 18, 2021
PubMed
Summary
This summary is machine-generated.

Bayesian statistics now allow researchers to rigorously conclude about the null hypothesis even with p > 0.05. This approach provides quantitative evidence for or against the alternative hypothesis, offering more insight than traditional methods.

Keywords:
Bayesian statisticsJASPProbability ratios of alternative to null hypotheses

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Area of Science:

  • Statistics
  • Psychology
  • Methodology

Background:

  • Traditional inferential statistics courses teach that the null hypothesis (no difference between groups) cannot be proven.
  • A p-value greater than 0.05 in a t-test or ANOVA traditionally yields inconclusive results regarding the null hypothesis.

Discussion:

  • Recent methodological advancements enable quantitative conclusions about the null hypothesis, even when p > 0.05.
  • Bayesian statistics offer a framework to assess the probability of the null hypothesis being true versus the alternative hypothesis of a difference.
  • This allows researchers to quantitatively determine if the null or alternative hypothesis is more probable.

Key Insights:

  • Bayesian analysis provides a rigorous method to interpret non-significant results (p > 0.05).
  • Researchers can now quantitatively conclude that the null hypothesis is unlikely or that the alternative hypothesis is more probable.
  • Conversely, Bayesian methods can also quantitatively support the likelihood of the null hypothesis.

Outlook:

  • This article introduces free resources and practical procedures for conducting Bayesian analysis.
  • Illustrative examples using t-tests and Analysis of Variance (ANOVA) are provided for applying Bayesian methods.
  • The adoption of Bayesian statistics can enhance the interpretation of research findings in psychology and other fields.