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Related Concept Videos

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...
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

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.
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
Significance Testing: Overview01:04

Significance Testing: Overview

Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
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...
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...

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Related Experiment Video

Updated: Jul 6, 2026

Tactile Semiautomatic Passive-Finger Angle Stimulator (TSPAS)
04:40

Tactile Semiautomatic Passive-Finger Angle Stimulator (TSPAS)

Published on: July 30, 2020

Type I error rates and power analyses for single-point sensitivity measures.

Caren M Rotello1, Michael E J Masson, Michael F Verde

  • 1Department of Psychology, University of Massachusetts, Amherst, Massachusetts 01003-7710, USA. caren@psych.umass.edu

Perception & Psychophysics
|April 1, 2008
PubMed
Summary

Researchers evaluated common sensitivity measures like d-prime (d’) and A-prime (A’) in experiments. Results show that while statistical power is similar across measures, Type I error rates are often unacceptably high, especially when not theoretically appropriate.

Related Experiment Videos

Last Updated: Jul 6, 2026

Tactile Semiautomatic Passive-Finger Angle Stimulator (TSPAS)
04:40

Tactile Semiautomatic Passive-Finger Angle Stimulator (TSPAS)

Published on: July 30, 2020

Area of Science:

  • Psychology
  • Cognitive Science
  • Psychophysics

Background:

  • Experiments frequently yield hit rates and false alarm rates across conditions.
  • These rates are often condensed into single-point sensitivity measures (e.g., d', A').
  • Statistical tests, such as t-tests, are commonly employed to assess experimental effects based on these measures.

Purpose of the Study:

  • To evaluate the Type I error rates and statistical power of commonly used single-point sensitivity measures.
  • To assess the performance of d', A', percent correct, and gamma.
  • To examine a newly proposed measure, gammaC, and various methods for handling edge cases (e.g., 0% false alarms or 100% hits).

Main Methods:

  • Large-scale Monte Carlo simulations were utilized to assess performance.
  • The study simulated experiments generating hit and false alarm rates.
  • Evaluated four established measures (d', A', percent correct, gamma) and one novel measure (gammaC).
  • Considered different approaches for handling boundary cases where false alarm rate = 0 or hit rate = 1.

Main Results:

  • Statistical power was found to be comparable across the evaluated sensitivity measures.
  • Type I error rates were frequently unacceptably high for several measures.
  • The choice of sensitivity measure significantly impacts Type I error rates.

Conclusions:

  • Current common sensitivity measures may lead to inflated Type I error rates in experimental data analysis.
  • Type I errors are minimized when the chosen sensitivity measure is theoretically aligned with the experimental data.
  • Careful selection of sensitivity measures is crucial for accurate experimental effect detection.