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Related Concept Videos

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...
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...
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...
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
One-Way ANOVA01:18

One-Way ANOVA

One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test01:09

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with data...

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Related Experiment Video

Updated: Jun 7, 2026

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
08:36

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

Inferential methods for comparing a single case with a control sample: modified t-tests versus mycroft et al.'s

John R Crawford1, Paul H Garthwaite, David C Howell

  • 1University of Aberdeen, UK.

Cognitive Neuropsychology
|November 2, 2010
PubMed
Summary

This study critiques a proposed modified ANOVA for single-case comparisons, arguing it has questionable assumptions and reduces statistical power. The original Crawford and Howell method remains valid for comparing patient performance to control samples.

Related Experiment Videos

Last Updated: Jun 7, 2026

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
08:36

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

Area of Science:

  • Psychology
  • Statistics

Background:

  • Existing inferential methods for single-case vs. control sample comparisons have been criticized.
  • Mycroft, Mitchell, and Kay (2002) proposed a modified ANOVA for such comparisons.

Purpose of the Study:

  • To evaluate the validity and implications of the modified ANOVA proposed by Mycroft et al.
  • To defend the inferential method developed by Crawford and Howell (1998).

Main Methods:

  • Critically analyze the assumptions underlying the modified ANOVA.
  • Compare the null hypotheses of both the Mycroft et al. and Crawford & Howell methods.
  • Examine the statistical consequences of employing the modified ANOVA.

Main Results:

  • The assumptions of the modified ANOVA are questionable and do not invalidate the Crawford & Howell method.
  • The modified ANOVA requires specific, potentially unrealistic, conditions regarding population variances and performance distributions.
  • The modified ANOVA unnecessarily reduces statistical power and demands hypothetical variance estimates.

Conclusions:

  • The modified ANOVA proposed by Mycroft et al. is statistically flawed and less powerful than existing methods.
  • The Crawford & Howell method provides a valid approach for single-case comparisons with control samples.
  • Researchers should exercise caution when considering the modified ANOVA for clinical or experimental data analysis.