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Related Concept Videos

Types of Hypothesis Testing01:11

Types of Hypothesis Testing

There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p ≠ 0.5.
Bonferroni Test01:10

Bonferroni Test

The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

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 population that is...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

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, comparing...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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...
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

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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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Published on: December 10, 2012

Bayes factor approaches for testing interval null hypotheses.

Richard D Morey1, Jeffrey N Rouder

  • 1Faculty of Behavioral and Social Sciences, University of Groningen, Groningen, the Netherlands. r.d.morey@rug.nl

Psychological Methods
|July 27, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces new Bayesian statistics methods for psychological hypothesis testing. These Bayes factor tests assess approximate constraints, improving upon existing methods for statistical equivalence testing.

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Area of Science:

  • Psychology
  • Statistics

Background:

  • Psychological theories function as constraints, and hypothesis testing in psychology evaluates these constraints against data.
  • Bayesian statistics offers a framework for comparing evidence between two hypotheses.
  • A challenge in hypothesis testing arises when constraints hold approximately, not exactly, leading to potential rejection of useful theories due to trivial deviations.

Purpose of the Study:

  • To develop novel Bayes factor (BF) 1-sample tests for evaluating approximate equality and ordinal constraints in psychological research.
  • To provide alternatives to existing BFs that do not accommodate interval null hypotheses.
  • To offer tools beneficial for researchers employing statistical equivalence testing.

Main Methods:

  • Development of several Bayes factor (BF) 1-sample tests.
  • Implementation of tests where the null hypothesis encompasses a small interval of negligible effect sizes around zero.
  • Focus on assessing approximate equality and ordinal constraints.

Main Results:

  • The proposed Bayes factor tests allow for the assessment of approximate theoretical constraints.
  • These methods provide an alternative to traditional hypothesis testing, particularly for statistical equivalence testing.
  • The developed tests address the limitation of exact constraints in previous BF approaches.

Conclusions:

  • The new Bayes factor tests effectively evaluate approximate equality and ordinal constraints in psychological data.
  • These methods enhance the utility of Bayesian hypothesis testing, especially for researchers focused on statistical equivalence.
  • Easy-to-use software is provided to encourage the adoption of these advanced statistical techniques.