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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.
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...

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...

Stratified Sampling Method

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...

Group Design

Group Design

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to...

Test for Homogeneity

Test for Homogeneity

The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can be stated as...

Decision Making: Traditional Method

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...

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Related Experiment Video

Updated: Jun 28, 2026

Barnes Maze Testing Strategies with Small and Large Rodent Models

Barnes Maze Testing Strategies with Small and Large Rodent Models

Published on: February 26, 2014

A flexible strategy for testing subgroups and overall population.

Mohamed Alosh¹, Mohammad F Huque

¹Division of Biometrics III, Office of Biostatistics, OTS, CDER, FDA, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USA. mohamed.Alosh@fda.hhs.gov

Statistics in Medicine

|November 6, 2008

Summary

Subgroup analyses in clinical trials often lack power and scientific rigor. This study introduces a new statistical method to improve subgroup analysis power and reliability while controlling errors.

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Published on: June 21, 2018

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Last Updated: Jun 28, 2026

Barnes Maze Testing Strategies with Small and Large Rodent Models

Barnes Maze Testing Strategies with Small and Large Rodent Models

Published on: February 26, 2014

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

Clinical Trials Methodology
Biostatistics
Statistical Inference

Background:

Subgroup analyses are common in clinical trials but often lack confirmatory power.
Existing methods for subgroup analysis face challenges in scientific soundness and statistical power.
Pitfalls in subgroup analysis limit the reliability of treatment effect findings.

Purpose of the Study:

To address common pitfalls in clinical trial subgroup analyses.
To introduce a flexible statistical strategy for testing hypotheses in both overall and subgroup populations.
To enhance the power and scientific rigor of subgroup effect detection.

Main Methods:

Investigated the power interplay between subgroup and total study population analyses.
Developed a flexible statistical strategy for a pre-specified sequence of hypothesis testing.
The method controls familywise Type I error rate and accounts for dependency between test statistics.

Main Results:

The proposed method offers higher power compared to traditional approaches.
Significance levels for subgroup analysis were determined.
The strategy allows subgroup testing contingent on pre-specified consistency with overall findings.

Conclusions:

The novel statistical strategy enhances the reliability and power of subgroup analyses in clinical trials.
This approach provides a scientifically sound framework for interpreting subgroup treatment effects.
Demonstrated application through retrospective analysis of three clinical trials.