Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

5.5K
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.
5.5K
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

479
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%...
479
Bonferroni Test01:10

Bonferroni Test

3.2K
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...
3.2K
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

90
Body:Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
90
Comparing Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

4.5K
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...
4.5K
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

7.4K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
7.4K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A pilot study of the <i>Watch Me Walk</i> program to increase the level of physical activity of older adults with intellectual disabilities.

Journal of intellectual disabilities : JOID·2026
Same author

Exploring Physical Activity Engagement and Related Variables During Pregnancy and Postpartum and the Best Practices for Self-Report Physical Activity Postpartum.

International journal of environmental research and public health·2025
Same author

Machine Learning to Detect Vocal Stereotypy: Improving Duration-Based Measures.

Behavior modification·2025
Same author

Natural Setting Interventions to Increase Physical Activity Level in Older Adults With Intellectual Disabilities: A Systematic Review.

Journal of applied research in intellectual disabilities : JARID·2025
Same author

Machine learning to detect schedules using spatiotemporal data of behavior: A proof of concept.

Journal of the experimental analysis of behavior·2025
Same author

Making physical activity fun and accessible to adults with intellectual disabilities: A pilot study of a gamification intervention.

Journal of applied research in intellectual disabilities : JARID·2024

Related Experiment Video

Updated: Dec 6, 2025

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.2K

How Many Tiers Do We Need? Type I Errors and Power in Multiple Baseline Designs.

Marc J Lanovaz1,2, Stéphanie Turgeon1

  • 1École de psychoéducation, Université de Montréal, C.P. 6128, succursale Centre-Ville, Montreal, QC H3C 3J7 Canada.

Perspectives on Behavior Science
|October 7, 2020
PubMed
Summary

Multiple baseline designs need at least three tiers with two showing clear changes for adequate statistical power and Type I error control. Requiring all tiers to show changes reduces study power significantly.

Keywords:
Error rateMultiple baseline designPowerSingle-case designVisual analysis

More Related Videos

Transcranial Direct Current Stimulation tDCS for Memory Enhancement
10:37

Transcranial Direct Current Stimulation tDCS for Memory Enhancement

Published on: September 18, 2021

15.2K
Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

17.0K

Related Experiment Videos

Last Updated: Dec 6, 2025

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.2K
Transcranial Direct Current Stimulation tDCS for Memory Enhancement
10:37

Transcranial Direct Current Stimulation tDCS for Memory Enhancement

Published on: September 18, 2021

15.2K
Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

17.0K

Area of Science:

  • Behavioral research methodology
  • Single-case experimental designs

Background:

  • Traditional guidelines suggest multiple baseline designs require at least three tiers demonstrating effects.
  • This recommendation lacks empirical validation, prompting the current investigation.

Purpose of the Study:

  • To empirically assess Type I error rate and statistical power in multiple baseline designs.
  • To evaluate the impact of the number of tiers showing a clear change on design accuracy.

Main Methods:

  • Generated 10,000 simulated multiple baseline graphs.
  • Applied the dual-criteria method to assess Type I error rate and power across varying numbers of effective tiers.
  • Conducted a visual inspection replication with two raters on 300 graphs.

Main Results:

  • Adequate Type I error rate (< .05) and power (> .80) were achieved when at least three tiers were present and two or more showed a clear change.
  • Requiring all tiers to demonstrate a clear change led to unacceptably low statistical power.

Conclusions:

  • Multiple baseline designs with at least three tiers and two clear changes offer a balance of statistical rigor and power.
  • Researchers should exercise caution regarding power limitations when demanding clear effects across all tiers in multiple baseline analyses.