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Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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

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...
Group Design02:01

Group Design

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to...
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to subjects...
Crossover Experiments01:16

Crossover Experiments

Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
Study Design in Statistics01:15

Study Design in Statistics

A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...

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Related Experiment Video

Updated: Jun 26, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Random assignment of intervention points in two-phase single-case designs: data-division-specific distributions.

Antonio Solanas1, Vicenta Sierra, Vicenç Quera

  • 1Departament de Metodologia de les Ciències del Comportament, Facultat de Psicologia, University of Barcelona, Passeig de la Vail d'Hebron, Spain. antonio.solanas@ub.edu

Psychological Reports
|December 24, 2008
PubMed
Summary

Randomization tests in single-case designs can produce false alarms with autocorrelated data. Applied researchers must be cautious when phase lengths differ significantly to avoid jeopardizing decisions.

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RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
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RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

Related Experiment Videos

Last Updated: Jun 26, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

Area of Science:

  • Behavioral Science
  • Psychology
  • Research Methodology

Background:

  • Single-case designs (SCDs) are crucial for evaluating interventions.
  • Randomization tests offer a non-parametric approach for analyzing SCD data.
  • The statistical properties of randomization tests in SCDs require further exploration, especially with autocorrelated data.

Purpose of the Study:

  • To investigate the statistical properties of randomization tests in two-phase (AB) single-case designs.
  • To examine the shape of randomization distributions based on intervention point assignment.
  • To assess the Type I error rates of randomization tests with autocorrelated data and unequal phase lengths.

Main Methods:

  • Explored statistical properties of randomization tests using random assignment of intervention points in AB designs.
  • Constructed randomization distributions using test statistic values for all possible assignments.
  • Investigated distribution shapes across different data divisions (intervention timing).
  • Tested for false alarms in autocorrelated data by comparing nominal and empirical Type I error rates.

Main Results:

  • The shape of randomization distributions varies depending on the intervention's introduction point.
  • Autocorrelated data can lead to an untenable assumption of exchangeability.
  • Type I error rates can be excessively high when one phase has significantly fewer measurements.
  • This increases the probability of detecting non-existent effects (false alarms).

Conclusions:

  • Randomization tests in AB single-case designs may yield unreliable results with autocorrelated data and unequal phase lengths.
  • Applied researchers must carefully consider phase length balance to avoid jeopardizing treatment effect conclusions.
  • The statistical validity of randomization tests is contingent on meeting specific data characteristics.