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Related Concept Videos

Types of Hypothesis Testing01:11

Types of Hypothesis Testing

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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...
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Statistical Hypothesis Testing01:16

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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...
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Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

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

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

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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,...
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Errors In Hypothesis Tests01:14

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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 Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Efficient alternatives for Bayesian hypothesis tests in psychology.

Sandipan Pramanik1, Valen E Johnson1

  • 1Department of Statistics, College of Science, Texas A&M University.

Psychological Methods
|April 14, 2022
PubMed
Summary
This summary is machine-generated.

Bayesian hypothesis testing can now support null hypotheses better. New "nonlocal" alternative hypotheses speed up evidence for both null and alternative hypotheses in psychology research.

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Area of Science:

  • Statistics
  • Psychological Research Methods

Background:

  • Bayesian hypothesis testing offers advantages over classical methods.
  • A key benefit is quantifying evidence for null hypotheses.
  • Current default Bayesian tests paradoxically hinder evidence for true null hypotheses.

Purpose of the Study:

  • To resolve the paradox in default Bayesian hypothesis testing.
  • To introduce "nonlocal" alternative hypotheses.
  • To enable faster accumulation of evidence for null and alternative hypotheses.

Main Methods:

  • Proposed the use of "nonlocal" alternative hypotheses.
  • Developed a new class of Bayesian hypothesis tests.

Main Results:

  • The proposed tests allow for more rapid evidence accumulation.
  • Evidence can be gathered faster for both true null and alternative hypotheses.
  • The approach is compatible with effect sizes of interest in psychology.

Conclusions:

  • "Nonlocal" alternative hypotheses resolve the paradox in Bayesian testing.
  • This method enhances the ability to find evidence for null hypotheses.
  • The approach supports psychological research by facilitating evidence for relevant effect sizes.