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Related Concept Videos

Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

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.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
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.
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...
Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
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...
Comparing Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...

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Related Experiment Video

Updated: May 13, 2026

A Within-Subject Experimental Design using an Object Location Task in Rats
09:28

A Within-Subject Experimental Design using an Object Location Task in Rats

Published on: May 6, 2021

Bayesian hypothesis testing for single-subject designs.

Rivka M de Vries1, Richard D Morey

  • 1Department of Psychometrics and Statistics, University of Groningen, Groningen, the Netherlands. r.m.de.vries@rug.nl

Psychological Methods
|March 6, 2013
PubMed
Summary
This summary is machine-generated.

Bayesian statistics offer new Bayes factor tests for single-subject designs. These methods quantify evidence for intervention effects in two-phase data, accounting for time-series dependencies.

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Area of Science:

  • Psychology
  • Statistics
  • Behavioral Science

Background:

  • Single-subject designs are crucial for evaluating interventions.
  • Assessing intervention effectiveness often involves analyzing score differences between phases.
  • Quantifying evidence for intervention effects in time-series data is challenging.

Purpose of the Study:

  • Introduce novel Bayesian Bayes factor tests for two-phase single-subject data.
  • Address serial dependency within the time-series data.
  • Provide methods to quantify evidence for intervention effects.

Main Methods:

  • Develop a time-series extension of the Jeffreys-Zellner-Siow Bayes factor for mean differences.
  • Introduce a time-series Bayes factor for testing differences in intercepts and slopes.
  • Models are related to interrupted time-series analysis.

Main Results:

  • The proposed Bayes factor tests effectively quantify evidence for intervention effects in single-subject time-series data.
  • These methods account for the temporal dependencies inherent in such data.
  • The tests allow for the assessment of differences in means, trends, intercepts, and slopes.

Conclusions:

  • Bayesian Bayes factor tests offer a robust approach to analyzing single-subject intervention data.
  • These methods provide a principled way to quantify evidence for intervention effects, considering serial dependency.
  • The introduced techniques enhance the analytical toolkit for researchers using single-subject designs in time-series contexts.