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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.
Hypothesis: Accept or Fail to Reject?01:17

Hypothesis: Accept or Fail to Reject?

The outcome of any hypothesis testing leads to rejecting or not rejecting the null hypothesis. This decision is taken based on the analysis of the data, an appropriate test statistic, an appropriate confidence level, the critical values, and P-values. However, when the evidence suggests that the null hypothesis cannot be rejected, is it right to say, 'Accept' the null hypothesis?
There are two ways to indicate that the null hypothesis is not rejected. 'Accept' the null hypothesis and 'fail to...
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...
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...
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test01:09

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with data...
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...

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Bayesian t tests for accepting and rejecting the null hypothesis.

Jeffrey N Rouder1, Paul L Speckman, Dongchu Sun

  • 1University of Missouri, Columbia, MO 65211, USA. rouderj@missouri.edu

Psychonomic Bulletin & Review
|March 19, 2009
PubMed
Summary
This summary is machine-generated.

Researchers can now use the Bayes factor, an alternative to the t test, to find evidence supporting the null hypothesis. This method offers a clear interpretation and improved statistical properties for scientific research.

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Area of Science:

  • Statistical inference
  • Scientific methodology

Background:

  • Scientific progress often relies on identifying invariances, frequently represented by null hypotheses.
  • Conventional significance testing cannot provide evidence for the null hypothesis.

Purpose of the Study:

  • Introduce the Bayes factor as an alternative to the conventional t test.
  • Enable researchers to quantify evidence for either the null or alternative hypothesis.

Main Methods:

  • Utilize the Bayes factor for statistical inference.
  • Develop a web-based program for easy Bayes factor calculation.

Main Results:

  • The Bayes factor allows for expressing preference for the null hypothesis.
  • Demonstrates superior properties compared to other inference methods in psychological research.

Conclusions:

  • The Bayes factor offers a natural and interpretable approach to hypothesis testing.
  • A user-friendly web tool is available to facilitate the application of Bayes factors in research.