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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.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...

Randomized Experiments

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Testing a Claim about Population Proportion

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...

Decision Making: P-value Method

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can have a...

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.
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Testing a Claim about Standard Deviation

Testing a Claim about Standard Deviation

A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
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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

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

Published on: September 11, 2021

Sample Size Determination for Decision-centered Pragmatic Trials.

Iztok Hozo¹, Lars G Hemkens², Benjamin Djulbegovic³

¹Department of Mathematics, Indiana University Northwest, Gary, Indiana, USA.

Journal of Clinical Epidemiology

|July 1, 2026

Summary

This summary is machine-generated.

This study introduces a decision-analytic method for determining sample size in pragmatic randomized trials (RCTs). This approach can significantly reduce participant numbers compared to traditional methods, making research more efficient and ethical.

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Published on: March 1, 2022

Area of Science:

Clinical Trials Methodology
Decision Analysis
Biostatistics

Background:

Pragmatic randomized trials (RCTs) are crucial for comparing real-world treatment options.
Determining appropriate sample size in pragmatic RCTs is challenging, balancing ethical considerations and resource allocation.
Existing methods for sample size determination in pragmatic RCTs are limited.

Purpose of the Study:

To develop a decision-analytic (DA) method for sample size determination in pragmatic RCTs.
To anchor sample size calculations to stakeholder-defined minimally important differences (MIDs).
To enable selection of the superior treatment based on net clinical benefit.

Main Methods:

Modeled a two-arm RCT using a decision tree incorporating treatment benefits and harms.
Weighted outcomes by stakeholder values and preferences (relative value, RV).
Set sample size to ensure expected loss from wrong decisions (γ·Δ) does not exceed acceptable regret (ARg).

Main Results:

The DA approach required approximately half the participants compared to conventional frequentist designs (mean N=89 vs. 168).
Sample size reductions in three of four recent pragmatic trials ranged from 47% to 72%.
The framework identified potential issues in one trial, highlighting the need to align effect size, stakeholder preferences, and decision risk.

Conclusions:

Designing pragmatic RCTs around decision quality offers transparency and ethical coherence.
The DA method can substantially reduce sample size, potentially lowering accrual barriers.
This approach accelerates the generation of actionable pragmatic evidence.