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Related Concept Videos

Sample Size Calculation01:19

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...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...

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Related Experiment Video

Updated: Jul 3, 2026

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

Sample size and correlational inference.

Richard B Anderson1, Michael E Doherty, Jeff C Friedrich

  • 1Department of Psychology, Bowling Green State University, Bowling Green, OH 43403, USA. randers@bgnet.bgsu.edu

Journal of Experimental Psychology. Learning, Memory, and Cognition
|July 9, 2008
PubMed
Summary
This summary is machine-generated.

Small samples can be more informative than large ones for detecting correlations, particularly in specific decision-making scenarios. This challenges traditional statistical assumptions, suggesting context influences sample size effectiveness.

Related Experiment Videos

Last Updated: Jul 3, 2026

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

Area of Science:

  • Cognitive Psychology
  • Statistical Inference
  • Decision Making

Background:

  • Traditional statistical theory posits larger samples yield more informative data for population inferences.
  • Previous theoretical work (Anderson et al., 2005) suggested potential advantages for smaller samples in specific correlation detection tasks.
  • The role of the informational environment's structure in sample informativeness requires further empirical investigation.

Purpose of the Study:

  • To test the hypothesis that informational environment structure can make small samples more informative than large ones for correlation inference.
  • To examine predictions derived from signal detection simulations concerning small-sample advantages.
  • To investigate the conditions under which small-sample advantages in correlation detection tasks occur.

Main Methods:

  • A simulation study was conducted to confirm and extend theoretical claims regarding small-sample advantages.
  • Three behavioral studies involved participants judging correlations from varying sample sizes (larger vs. smaller).
  • Participants determined if data pairs originated from populations with zero or non-zero correlations.

Main Results:

  • Simulation results confirmed that small-sample advantages in yes/no correlation detection tasks are possible under specific decision conditions.
  • Behavioral studies generally showed higher accuracy with larger samples, aligning with traditional statistical theory.
  • A small-sample advantage was observed in one specific experimental condition.

Conclusions:

  • The structure of the informational environment can influence the relative informativeness of sample sizes in correlation detection.
  • Small-sample advantages in correlation inference are context-dependent and occur under particular decision conditions.
  • Findings contribute to understanding decision-making paradigms and challenge universally accepted sample size heuristics.