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Related Concept Videos

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...
P-value01:10

P-value

P-value is one of the most crucial concepts in statistics.
P-value stands for the probability value.  P-value is the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample.
A large P-value calculated from the data indicates to  not reject the null hypothesis. But a higher P-value does not mean that the null hypothesis is true. The smaller the P-value, the more unlikely...
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...
Significance Testing: Overview01:04

Significance Testing: Overview

Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
Decision Making: P-value Method01:09

Decision Making: P-value Method

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 have a...
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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Related Experiment Video

Updated: Jun 24, 2026

Isolating Interaction-Null/Impaired Mutants Using the Yeast Two-Hybrid Assay
02:44

Isolating Interaction-Null/Impaired Mutants Using the Yeast Two-Hybrid Assay

Published on: December 29, 2023

The importance of proving the null.

C R Gallistel1

  • 1Rutgers University, Piscataway, NJ 08854, USA. galliste@ruccs.rutgers.edu

Psychological Review
|April 8, 2009
PubMed
Summary

Bayesian analysis can support null hypotheses, unlike conventional methods. A sensitivity analysis helps determine if data favor the null by assessing the vagueness of alternative hypotheses.

Area of Science:

  • Statistics
  • Bayesian Inference
  • Hypothesis Testing

Background:

  • Null hypotheses are crucial for scientific theory but difficult to support with conventional statistics.
  • Bayesian analysis offers a framework for directly evaluating null hypotheses.

Purpose of the Study:

  • To present a general Bayesian method for supporting null hypotheses.
  • To introduce a sensitivity analysis for evaluating the null hypothesis against vague alternatives.
  • To demonstrate the method with common experimental questions.

Main Methods:

  • Formulating a vague alternative hypothesis in Bayesian analysis.
  • Conducting a sensitivity analysis by varying the vagueness of the alternative.
  • Calculating the odds for or against the null hypothesis.

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Generation of Null Mutants to Elucidate the Role of Bacterial Glycosyltransferases in Bacterial Motility
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Generation of Null Mutants to Elucidate the Role of Bacterial Glycosyltransferases in Bacterial Motility

Published on: March 11, 2022

Related Experiment Videos

Last Updated: Jun 24, 2026

Isolating Interaction-Null/Impaired Mutants Using the Yeast Two-Hybrid Assay
02:44

Isolating Interaction-Null/Impaired Mutants Using the Yeast Two-Hybrid Assay

Published on: December 29, 2023

Generation of Null Mutants to Elucidate the Role of Bacterial Glycosyltransferases in Bacterial Motility
12:29

Generation of Null Mutants to Elucidate the Role of Bacterial Glycosyltransferases in Bacterial Motility

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Main Results:

  • The vaguer the alternative hypothesis, the more the null hypothesis is favored.
  • If odds for the null approach 1 as the maximum effect size approaches 0, data support the null.
  • The method is illustrated with examples addressing equality of means, performance at chance, and additivity of factors.

Conclusions:

  • Bayesian sensitivity analysis provides a robust approach to support null hypotheses.
  • This method allows for intuitive graphical representation and simple computations.
  • It offers a valuable tool for researchers across various scientific disciplines.