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Related Concept Videos

Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
Comparing Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

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...
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
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...
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

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...
Bonferroni Test01:10

Bonferroni Test

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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Related Experiment Video

Updated: May 14, 2026

Behavioral Assessment of Hearing in 2 to 4 Year-old Children: A Two-interval, Observer-based Procedure Using Conditioned Play-based Responses
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Behavioral Assessment of Hearing in 2 to 4 Year-old Children: A Two-interval, Observer-based Procedure Using Conditioned Play-based Responses

Published on: January 23, 2017

Individualized two-stage multiple testing procedures with corresponding interval estimates.

Arthur Cohen1, Yingqiu Ma, Harold B Sackrowitz

  • 1Department of Statistics and Biostatistics, Hill Center, Rutgers University, Piscataway, NJ 08854, USA.

Biometrical Journal. Biometrische Zeitschrift
|February 8, 2013
PubMed
Summary

This study introduces a novel method for multiple testing procedures (MTPs) that simplifies the creation of interval estimates. These new MTPs offer improved interval properties compared to traditional stepwise methods.

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Behavioral Assessment of Hearing in 2 to 4 Year-old Children: A Two-interval, Observer-based Procedure Using Conditioned Play-based Responses
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Testing for Metacognitive Responding Using an Odor-based Delayed Match-to-Sample Test in Rats

Published on: June 18, 2018

Area of Science:

  • Statistics
  • Statistical modeling

Background:

  • Multiple testing procedures (MTPs) are crucial in statistical applications for testing numerous hypotheses.
  • Traditional stepwise procedures often lead to overly conservative results and lack computationally feasible interval estimates.
  • Developing practical interval estimates for stepwise MTPs has been a significant research challenge.

Purpose of the Study:

  • To present an alternative method for constructing multiple testing procedures (MTPs).
  • To develop MTPs that easily admit corresponding interval estimates.
  • To address the limitations of existing stepwise procedures regarding interval estimation.

Main Methods:

  • Developed a new method for constructing multiple testing procedures (MTPs).
  • Focused on individual hypothesis testing while utilizing all available data.
  • Ensured the method easily accommodates corresponding interval estimates.

Main Results:

  • The new MTPs perform comparably to commonly used stepwise procedures.
  • These MTPs offer practical interval properties, including desirable convexity of acceptance regions.
  • Interval estimates derived from this method are easily obtained and possess advantageous characteristics.

Conclusions:

  • The proposed MTPs provide a viable alternative to stepwise procedures, particularly in dependent cases.
  • The associated interval estimates are typically shorter, more informative, and less prone to falsely including the null point than those from Bonferroni, Scheffé, Tukey, or Dunnett methods.
  • This approach enhances the practical utility of multiple testing by providing accessible and superior interval estimates.