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Related Concept Videos

Bonferroni Test01:10

Bonferroni Test

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The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of 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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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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Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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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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Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

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A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
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Comparing Experimental Results: Student's t-Test01:09

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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...
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Statistical Guideline #7 Adjust Type 1 Error in Multiple Testing.

Ren Liu1

  • 1Quantitative Methods, Measurement, and Statistics (QMMS), University of California, Merced, CA, 95343, USA. rliu45@ucmerced.edu.

International Journal of Behavioral Medicine
|February 28, 2022
PubMed
Summary
This summary is machine-generated.

This series provides statistical guidelines for behavioral medicine research, aiming to improve data analysis and presentation in the International Journal of Behavioral Medicine (IJBM). These guidelines will be integrated into the journal for researchers.

Keywords:
BonferroniBootstrapMultiple testingPowerType 1 errorp-valueα value

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Area of Science:

  • Behavioral Medicine
  • Biostatistics
  • Scientific Publishing

Background:

  • Behavioral medicine research often requires rigorous statistical analysis.
  • Clear presentation of data is crucial for scientific reproducibility and impact.
  • Existing statistical guidelines may not fully address the nuances of behavioral research.

Purpose of the Study:

  • To establish a series of statistical guidelines for behavioral medicine research.
  • To outline appropriate methods for data analysis and presentation.
  • To create a resource for authors submitting to the International Journal of Behavioral Medicine (IJBM).

Main Methods:

  • The series will highlight common statistical considerations.
  • Guidance will focus on appropriate data analysis techniques.
  • Emphasis will be placed on effective data presentation strategies.

Main Results:

  • The series aims to culminate in a set of basic statistical guidelines.
  • These guidelines will be adopted by the International Journal of Behavioral Medicine (IJBM).
  • The guidelines will serve as an independent resource for researchers.

Conclusions:

  • Implementing standardized statistical guidelines will enhance the quality of behavioral medicine research.
  • Clearer data analysis and presentation will improve the interpretability of findings.
  • The developed guidelines will support both IJBM authors and the broader research community.