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Related Concept Videos

Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

7.1K
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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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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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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Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

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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%...
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Decision Making: Traditional Method01:14

Decision Making: Traditional Method

5.7K
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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Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

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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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Hypothesis Testing and Model Comparison in Two-level Structural Equation Models.

S Y Lee, X Y Song

    Multivariate Behavioral Research
    |January 30, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces the Bayes factor for hypothesis testing and model comparison in two-level structural equation models. This flexible Bayesian approach aids in selecting appropriate models for complex data, improving upon existing methods.

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    Area of Science:

    • Statistics
    • Psychometrics
    • Bayesian Inference

    Background:

    • Two-level structural equation modeling (SEM) is crucial for analyzing hierarchical data.
    • Model selection and hypothesis testing are fundamental challenges in SEM.
    • Existing goodness-of-fit methods can present limitations.

    Purpose of the Study:

    • To demonstrate the application of the Bayes factor for hypothesis testing and model comparison in general two-level SEM.
    • To present a flexible Bayesian methodology applicable to various nonnested models.
    • To address limitations of current goodness-of-fit assessment techniques.

    Main Methods:

    • Utilizing the Bayes factor, a Bayesian inferential tool, for model comparison.
    • Applying the methodology to general two-level structural equation models.
    • Illustrating the approach with real-world data from an AIDS care study.

    Main Results:

    • The proposed Bayes factor methodology is effective for hypothesis testing in two-level SEM.
    • The approach demonstrates flexibility, accommodating a wide range of nonnested models.
    • The method offers solutions to problems associated with existing goodness-of-fit assessments.

    Conclusions:

    • The Bayes factor provides a robust and flexible framework for model comparison in two-level SEM.
    • This Bayesian approach enhances the assessment of complex hierarchical data structures.
    • The methodology offers practical advantages for researchers in various fields, including health studies.