Research Topics:

Quantitative Research

Industry/Market Focus:

Consumers

Content Type:

Glossary

Allowable Sampling Error Definition

The amount of sampling error the researcher is willing to accept.

Allowable sampling error refers to the degree of acceptable uncertainty or variation in sample results, as compared to the true population parameter. The size of error impacts the validity and generalizability of research findings. Simply put, the lower the sample error, the higher the likely reliability of the findings. A lower sample size provides strong evidence of the conclusion. Conversely, a larger allowable error can reduce confidence in the findings.

Who relies on the allowable sampling error?

Researchers, statisticians, pollsters and data analysts use allowable sampling error when conducting surveys and experiments and collecting data from a larger population. The allowable sampling error is crucial in inferences about a whole population based on a smaller sample.

Why should I care about the allowable sampling error?

Utilizing allowable sampling error is key to assessing the reliability and accuracy of the conclusions drawn from a sample. If the allowable error is determined to be too high, findings may not accurately represent the population, which leads to the lack of credibility in findings. Lower allowable error means the sample results are more trustworthy and representative of the population, increasing the validity of the findings.