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

The Representativeness Heuristic

The Representativeness Heuristic

The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.

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...

Sampling Methods: Overview

Sampling Methods: Overview

A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling.
In analytical chemistry, the choice of sampling...

Systematic Sampling Method

Systematic Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...

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...

Sample Proportion and Population Proportion

Sample Proportion and Population Proportion

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...

You might also read

Related Articles

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

Sort by

Same author

Circling around number: People can accurately extract numeric values from circle area ratios.

Psychonomic bulletin & review·2022

Same author

Task Constraints Affect Mapping From Approximate Number System Estimates to Symbolic Numbers.

Frontiers in psychology·2018

Same author

Sample size weighting follows a curvilinear function.

Journal of experimental psychology. Learning, memory, and cognition·2018

Same author

Fractions as percepts? Exploring cross-format distance effects for fractional magnitudes.

Cognitive psychology·2015

Same author

What counts? Visual and verbal cues interact to influence what is considered a countable thing.

Memory & cognition·2015

Same author

Knowledge on the line: manipulating beliefs about the magnitudes of symbolic numbers affects the linearity of line estimation tasks.

Psychonomic bulletin & review·2013

Search research articles

Related Experiment Video

Updated: May 14, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Sample representativeness affects whether judgments are influenced by base rate or sample size.

Natalie A Obrecht¹, Dana L Chesney

¹Department of Psychology, William Paterson University, 300 Pompton Road, Wayne, NJ 07470, USA. obrechtn@wpunj.edu

Acta Psychologica

|February 21, 2013

Summary

People can use base rate and sample size information for probability judgments. Judgments depend on whether samples are linked to specific subpopulations, with numeracy influencing this decision.

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

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

Related Experiment Videos

Last Updated: May 14, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

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

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

Area of Science:

Cognitive Psychology
Decision Making
Probability Theory

Background:

Understanding how individuals combine statistical information is crucial for decision-making research.
Base rates and sample size are key factors influencing probability judgments.
Previous research has explored the use of these factors, but their interplay requires further investigation.

Purpose of the Study:

To investigate how people utilize base rate and sample size information when making probability judgments.
To determine the conditions under which base rate neglect or utilization occurs.
To examine the role of numeracy in the integration of statistical information.

Main Methods:

Participants were presented with two samples from an animal population.
They were asked to estimate the probability of an animal being aggressive.
Experimental conditions varied the linkage of samples to specific subpopulations and provided base rate information.

Main Results:

Judgments were influenced by subpopulation base rates when explicitly linked to specific samples.
When samples were not identified as originating from different subpopulations, judgments primarily reflected sample size information.
Higher numeracy correlated with greater use of both base rate and sample size information.

Conclusions:

Individuals can effectively integrate base rate and sample size information for inferential judgments.
People can discern when to apply base rates based on contextual sampling cues.
The ability to simultaneously consider base rate and sample size information is demonstrated, moderated by numeracy.