Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Sampling Plans

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...

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

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...

Sample Size Calculation

Sample Size Calculation

Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...

Methods of Medium Optimization

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

Randomized Experiments

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...

Bioavailability Study Design: Single Versus Multiple Dose Studies

Bioavailability Study Design: Single Versus Multiple Dose Studies

Bioavailability studies are essential for understanding how a drug is absorbed, distributed, metabolized, and excreted in the body. These studies assess the extent and rate at which the active pharmaceutical agent becomes available at the site of action. The design of bioavailability studies can involve single-dose or multiple-dose regimens, each with distinct advantages and limitations.Single-dose studies are the preferred approach due to their simplicity and reduced drug exposure for...

You might also read

Related Articles

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

Sort by

Same author

Point-biserial correlation: Interval estimation, hypothesis testing, meta-analysis, and sample size determination.

The British journal of mathematical and statistical psychology·2019

Same author

Interval estimation for linear functions of medians in within-subjects and mixed designs.

The British journal of mathematical and statistical psychology·2019

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 15, 2026

Sampling Soils in a Heterogeneous Research Plot

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

Sample size planning for studies with multiple measurement trials.

Douglas G Bonett¹

¹Department of Psychology, University of California, Santa Cruz.

Psychological Methods

|July 13, 2026

Summary

Increasing measurement trials in studies enhances data reliability, potentially reducing the number of participants needed. This research provides sample size formulas for optimizing study design with multiple measurement trials.

More Related Videos

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

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

Published on: September 11, 2021

Related Experiment Videos

Last Updated: Jul 15, 2026

Sampling Soils in a Heterogeneous Research Plot

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

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

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

Published on: September 11, 2021

Area of Science:

Statistics
Research Methodology
Psychology

Background:

Studies involving multiple measurement trials rely on participant numbers and trial counts as key design elements.
The dependent variable in such studies is typically the average of repeated measurements per participant.

Purpose of the Study:

To derive closed-form sample size formulas for studies using multiple measurement trials.
To aid researchers in determining optimal sample sizes for desired statistical precision and power.

Main Methods:

Development of sample size formulas for two-group and paired-samples designs.
Extension of formulas to general linear contrasts in between-subjects and within-subjects designs.
Provision of R functions for practical application of the derived formulas.

Main Results:

Formulas are derived to calculate the necessary sample size based on the number of measurement trials.
Increased measurement trials lead to higher reliability, reducing required sample size for a given precision or power.

Conclusions:

The derived formulas offer a method for optimizing sample size in studies with multiple measurement trials.
Researchers can use these formulas to balance participant numbers and measurement trials for efficient study design.