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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...
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...
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:
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...

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Updated: Jun 23, 2026

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

Published on: September 11, 2021

Outcome measures and implications for sample-size calculations.

Michael Zlowodzki1, Mohit Bhandari

  • 1Department of Orthopaedic Surgery, University of Minnesota, 2450 Riverside Avenue South, R200, Minneapolis, MN 55454, USA. zlowo001@umn.edu

The Journal of Bone and Joint Surgery. American Volume
|May 5, 2009
PubMed
Summary
This summary is machine-generated.

Selecting appropriate outcome measures is crucial for clinical study success. This guide covers choosing between primary and secondary outcomes, understanding different data types, and calculating sample sizes for robust research.

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Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

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Published on: September 11, 2021

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

Area of Science:

  • Clinical research methodology
  • Biostatistics
  • Study design

Background:

  • The selection of outcome measures significantly influences clinical study design and statistical power.
  • Variability in sample-size requirements is directly linked to the chosen outcome measures and their quantity.

Purpose of the Study:

  • To provide guidance on selecting appropriate outcome measures for clinical studies.
  • To elucidate the distinctions between various outcome measure types and their implications.
  • To offer instructions for sample-size calculations.

Main Methods:

  • Review of categorical versus continuous outcomes.
  • Explanation of primary versus secondary outcomes.
  • Discussion of minimally important differences and composite outcomes.
  • Guidance on sample-size calculation procedures.

Main Results:

  • Understanding outcome measure types is essential for appropriate study design.
  • The number and type of outcome measures directly impact sample-size needs.
  • Minimally important differences and composite outcomes present specific challenges in interpretation.

Conclusions:

  • Proper selection of outcome measures is fundamental for valid clinical study results.
  • Accurate sample-size calculations are necessary to ensure adequate statistical power.
  • Consideration of outcome measure characteristics prevents common statistical pitfalls.