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

Sample Size Calculation01:19

Sample Size Calculation

3.7K
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...
3.7K
Sampling Plans01:23

Sampling Plans

254
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...
254
Group Design02:01

Group Design

9.5K
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...
9.5K
Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

5.6K
Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
5.6K
Sampling Distribution01:12

Sampling Distribution

13.6K
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
13.6K
Contaminants and Errors01:16

Contaminants and Errors

135
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
135

You might also read

Related Articles

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

Sort by
Same author

An ecological text message experiment of Hispanic and Latino adolescents' recognition of and emotional responses to digital dating abuse behaviors.

Journal of research on adolescence : the official journal of the Society for Research on Adolescence·2026
Same author

A Confidence Interval for the Difference Between Standardized Regression Coefficients.

Multivariate behavioral research·2024
Same author

A sequential approach for noninferiority or equivalence of a linear contrast under cost constraints.

Psychological methods·2023
Same author

Questionable research practices and cumulative science: The consequences of selective reporting on effect size bias and heterogeneity.

Psychological methods·2023
Same author

Validity and reliability of the Body-Esteem Scale among a diverse sample of preadolescent youth.

Psychological assessment·2023
Same author

Appropriately estimating the standardized average treatment effect with missing data: A simulation and primer.

Behavior research methods·2022
Same journal

Bayesian evaluation for latent variable models: A tutorial on computing information criteria and bayes factors with the r package bleval.

Psychological methods·2026
Same journal

A stochastic block prior for clustering in graphical models.

Psychological methods·2026
Same journal

Three-level vector autoregressive models.

Psychological methods·2026
Same journal

Scaling cognitive modeling to big data: A deep learning approach to studying individual differences in evidence accumulation model parameters.

Psychological methods·2026
Same journal

Best practices in multilevel modeling for within-cluster group comparisons: An evaluation of coding strategies reflecting group composition and heterogeneity.

Psychological methods·2026
Same journal

A unified framework for psychometrics in experimental psychology: The standardized generalized hierarchical factor model.

Psychological methods·2026
See all related articles

Related Experiment Video

Updated: Sep 4, 2025

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan
11:58

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan

Published on: June 29, 2018

9.5K

Sample size planning for replication studies: The devil is in the design.

Samantha F Anderson1, Ken Kelley2

  • 1Department of Psychology, Arizona State University.

Psychological Methods
|July 21, 2022
PubMed
Summary
This summary is machine-generated.

Replication success is not just about p-values. This study introduces four replication goals and provides sample size planning methods for each, moving beyond dichotomous significance testing for more robust scientific progress.

More Related Videos

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

34.8K
Genome-wide Determination of Mammalian Replication Timing by DNA Content Measurement
08:06

Genome-wide Determination of Mammalian Replication Timing by DNA Content Measurement

Published on: January 19, 2017

8.6K

Related Experiment Videos

Last Updated: Sep 4, 2025

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan
11:58

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan

Published on: June 29, 2018

9.5K
Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

34.8K
Genome-wide Determination of Mammalian Replication Timing by DNA Content Measurement
08:06

Genome-wide Determination of Mammalian Replication Timing by DNA Content Measurement

Published on: January 19, 2017

8.6K

Area of Science:

  • Psychology and social sciences
  • Scientific methodology
  • Research integrity

Background:

  • Replication is crucial for scientific advancement, but recent failures have increased scrutiny.
  • Current replication assessment often relies on dichotomous null hypothesis significance testing (NHST), labeling studies as success or failure based on p < .05.
  • Existing work on replication success primarily addresses the analysis phase, neglecting crucial design elements like sample size planning.

Purpose of the Study:

  • To address the need for improved sample size planning in replication studies.
  • To introduce and define four distinct goals for conducting replication studies.
  • To provide formal methods for sample size planning tailored to each of the four replication goals.

Main Methods:

  • The study outlines four different conceptual goals for replication.
  • Formalized sample size planning procedures are developed for each identified replication goal.
  • Practical examples and syntax are provided to demonstrate the application of these planning methods.

Main Results:

  • The article presents a framework for sample size planning that aligns with diverse replication objectives.
  • It offers guidance on sample size determination beyond traditional null hypothesis significance testing.
  • The proposed methods aim to enhance the rigor and clarity of replication study designs.

Conclusions:

  • Effective sample size planning is essential for achieving specific replication goals.
  • Moving beyond dichotomous success/failure metrics enhances the utility of replication studies.
  • The provided methods and examples offer practical tools for researchers to improve replication design and interpretation.