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Related Concept Videos

Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
Decision Making: P-value Method01:09

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...
Testing a Claim about Population Proportion01:24

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...
Testing a Claim about Mean: Known Population SD01:11

Testing a Claim about Mean: Known Population SD

A complete procedure of testing the hypothesis about a population mean is explained here.
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
In most realistic situations, the population standard deviation is often unknown, but in rare circumstances, when it...
Testing a Claim about Standard Deviation01:19

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.
As a first step, the hypothesis (null and alternative) concerning the claim about...
Testing a Claim about Mean: Unknown Population SD01:21

Testing a Claim about Mean: Unknown Population SD

A complete procedure of testing a hypothesis about a population mean when the population standard deviation is unknown is explained here.
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used; instead...

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Related Experiment Video

Updated: Jul 12, 2026

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
13:55

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A SAS procedure for exact probability testing of difference between sample and population proportion.

J Lee1, H P Lee, N P Fong

  • 1Department of Community, Occupational and Family Medicine, National University of Singapore.

Computers in Biology and Medicine
|January 1, 1989
PubMed
Summary

Biomedical researchers can now use an exact binomial probability test for more reliable statistical analysis. This method offers accurate results, especially with small sample sizes, unlike traditional normal theory approximations.

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Area of Science:

  • Biostatistics
  • Medical Research Methodology
  • Statistical Inference

Background:

  • Hypothesis testing for population proportions (P) against a specified value (P0) is common in biomedical research.
  • Assessing attributes like postoperative wound infection rates requires accurate statistical methods.
  • Traditional normal theory approximation for significance testing can be unreliable with small sample sizes or extreme proportions.

Purpose of the Study:

  • To introduce a reliable statistical testing procedure for population proportions.
  • To address the limitations of normal theory approximation in specific scenarios.
  • To provide a computational tool for implementing the exact binomial probability test.

Main Methods:

  • Utilizing the exact binomial probability procedure for hypothesis testing.
  • Developing a computer program in SAS to execute the exact probability test.
  • Comparing the exact method with the normal theory approximation.

Main Results:

  • The exact binomial probability test provides reliable results, particularly when normal theory approximations fail.
  • The SAS program facilitates the practical application of this accurate statistical method.
  • Demonstrated the unreliability of normal theory approximation in small sample size scenarios.

Conclusions:

  • The exact binomial probability test is a superior method for hypothesis testing of population proportions in biomedical research.
  • The developed SAS program offers a robust solution for accurate statistical analysis.
  • Emphasizes the importance of choosing appropriate statistical methods for reliable research findings.