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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...
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.
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.
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...
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...

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Related Experiment Video

Updated: Jul 25, 2026

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
16:23

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Published on: February 26, 2014

Answering two criticisms of hypothesis testing: a comment.

R C Serlin1

  • 1University of Wisconsin-Madison, USA. rcserlin@foestaff.wisc.edu

Psychological Reports
|November 22, 2000
PubMed
Summary

This study examines the validity of one-tailed and directional two-tailed hypothesis tests. While generally valid, the falseness of null hypotheses impacts the directional two-tailed test

Area of Science:

  • Statistical Methods
  • Hypothesis Testing

Background:

  • Leventhal (1999) addressed criticisms regarding the validity of hypothesis testing procedures.
  • The study focuses on one-tailed and directional two-tailed tests in statistical analysis.

Purpose of the Study:

  • To evaluate the validity of one-tailed and directional two-tailed tests when point null hypotheses are false.
  • To determine if hypothesis tests can provide the probability of correct decisions.

Main Methods:

  • Review and analysis of Leventhal's (1999) arguments on hypothesis testing.
  • Examination of the impact of false point null hypotheses on test operating characteristics.

Main Results:

  • One-tailed and directional two-tailed tests are shown to be valid even if all point null hypotheses are false.

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  • Hypothesis tests can offer probabilities for the correctness of decisions made.
  • The falseness of all point null hypotheses negatively affects the directional two-tailed test's operating characteristics.

    • Conclusions:

    • Leventhal's arguments for the directional two-tailed test are potentially weakened by the impact of false null hypotheses.
    • Further consideration of test operating characteristics is needed when null hypotheses are not strictly true.