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Stratified Sampling Method01:16

Stratified 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. 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.
To choose a stratified sample, divide the population into groups called strata and then take a...
Classification of Epithelial Tissues: Stratified Epithelium01:29

Classification of Epithelial Tissues: Stratified Epithelium

Stratified epithelium consists of several stacked layers of cells. They provide the durability to withstand constant physical and chemical attacks. Stratified epithelium is named after the shape of the most apical layer of cells. Stratified squamous epithelium is the most common type found in the human body. In this tissue, the apical cells are squamous, whereas the basal layer contains either columnar or cuboidal cells. The basal cells divide to form new daughter cells, which gradually become...
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure 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.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Randomized Experiments01:13

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.
Simple randomization
Simple...
Sampling Plans01:23

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...
Response Surface Methodology01:16

Response Surface Methodology

Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:

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Related Experiment Video

Updated: May 24, 2026

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

How many strata in an RCT? A flexible approach.

P Silcocks1

  • 1CRUK Liverpool Cancer Trials Unit, University of Liverpool, Liverpool L69 3GL, UK. paul.silcocks@liverpool.ac.uk

British Journal of Cancer
|March 15, 2012
PubMed
Summary

This study introduces a flexible method for determining the optimal number of strata in cancer clinical trials, balancing prognostic factors with overstratification risks. The approach aids researchers in designing robust and adaptable trial strategies.

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Epidemiological Research

Background:

  • Prognostic factors are crucial in cancer trial design and analysis.
  • Overstratification must be avoided by limiting the number of strata.
  • Existing guidance can be improved with explicit principles and adaptability.

Purpose of the Study:

  • To propose a new method for determining the number of strata in clinical trials.
  • To provide clinicians with a practical tool for trial design.
  • To balance the inclusion of prognostic factors with the risk of overstratification.

Main Methods:

  • Utilizes a Poisson distribution to model observations per stratum.
  • Calculates the number of strata based on sample size, minimum stratum size, and acceptable risk.

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Last Updated: May 24, 2026

Sampling Soils in a Heterogeneous Research Plot
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  • Programmable in Excel for ease of use.

    • Main Results:

    • For a 250-patient trial, <1% risk of <10 patients/stratum suggests no more than 13 strata.
    • For survival analysis with 170 deaths, no more than 9 strata are prudent.
    • This can often be achieved using just two prognostic variables.

    Conclusions:

    • The proposed method is flexible and principle-based.
    • Applicable to both clinical trial design and epidemiological studies.
    • A valuable tool for researchers and clinicians.