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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.
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Central Limit Theorem

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The central limit theorem, abbreviated as clt, is one of the most powerful and useful ideas in all of statistics. The central limit theorem for sample means says that if you repeatedly draw samples of a given size and calculate their means, and create a histogram of those means, then the resulting histogram will tend to have an approximate normal bell shape. In other words, as sample sizes increase, the distribution of means follows the normal distribution more closely.
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Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

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Estimating sample size in conceptual property norms by standardizing coverage.

Enrique Canessa¹, Sergio E Chaigneau², Rodrigo Lagos²

¹Escuela de Ingeniería Informática, Universidad de Valparaíso, General Cruz 222, Valparaíso, Chile. enrique.canessa@uv.cl.

Behavior Research Methods

|July 13, 2026

Summary

Conceptual properties norming (CPN) studies should prioritize coverage over fixed participant counts. This ensures more accurate semantic profiles by accounting for concept richness, improving data reliability and comparability.

Keywords:

Conceptual properties norming studies Coverage Property listing task Recommended target coverage Sample size estimation

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Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

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Published on: September 27, 2019

Area of Science:

Cognitive Science
Psycholinguistics
Computational Linguistics

Background:

Conceptual properties norming (CPN) studies are vital for understanding mental representations.
Traditional CPN methods standardize participant numbers, overlooking concept-specific semantic richness.

Purpose of the Study:

Propose a novel framework for CPN studies based on coverage, not participant count.
Develop a method to estimate sample sizes for achieving target coverage levels.
Enhance the accuracy, comparability, and replicability of semantic profiles.

Main Methods:

Developed a mathematical framework to estimate sample size based on coverage.
Validated models using empirical data from concrete and abstract concepts.
Created pre-calculated look-up tables and an interactive HTML coverage calculator.

Main Results:

Sample sizes for robust coverage vary significantly across concepts.
Fixed-participant approaches in CPN studies are limited.
Coverage-based sampling yields more reliable semantic data.

Conclusions:

Standardizing coverage is superior to fixed participant counts in CPN studies.
Recommended coverage target of 0.70–0.80 for robust analysis.
A two-stage sampling strategy is proposed for practical implementation.