sample size

USAGE SUMMARY

The phrase "sample size" is correct and usable in written English.
It is typically used in research and statistics to refer to the number of observations or data points collected for a study or experiment. Example: "In our study, we found that a larger sample size led to more reliable results."

✓ Grammatically correct

Science

News & Media

Academia

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

Sample size and no sample size.

The New York Times - Sports

Sample size = 9/group.

Nature

n indicates sample size.

Nature

It could be sample size.

The New York Times

■ Pay attention to sample size.

The New York Times

Sample size: one million users.

The Guardian - Tech

Sample size (n) is indicated.

Nature

Small sample size, the puckmetricians would say.

The New Yorker

The overall sample size was 38,974.

The Guardian - Opinion

Small Sample Size Theater ladies and gentlemen!

The Guardian - Sport

I wonder at the sample size.

The Guardian - Opinion

Expert writing Tips

Best practice

When reporting research results, always explicitly state the "sample size" to ensure transparency and allow for proper interpretation of the findings.

Common error

Avoid drawing broad conclusions from studies with small "sample sizes". Small samples may not accurately represent the larger population, leading to unreliable results. A larger sample generally increases the reliability of the results.

Linguistic Context

The phrase "sample size" functions as a noun phrase, typically acting as the subject or object of a sentence. It refers to the number of observations used in a study or experiment, as exemplified in the Ludwig examples where studies report on "sample size" to quantify their research scope. Ludwig AI confirms that it's a correctly used phrase.

Expression frequency: Very common

Frequent in

Science

47%

News & Media

32%

Academia

12%

Less common in

Formal & Business

3%

Wiki

0%

Encyclopedias

0%

Ludwig's WRAP-UP

The phrase "sample size" is a noun phrase used to quantify the extent of data in research. Ludwig AI confirms its correct and frequent usage, particularly within scientific, academic, and news contexts. Proper use involves stating it explicitly when reporting research and avoiding overgeneralizations from small samples. Alternatives include "number of participants" or "data set size", depending on the specific nuance you intend to convey. Understanding its importance ensures transparent and reliable communication of research findings.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

number of participants

Focuses on the individuals involved in a study, rather than the overall size of the data.

study population

Emphasizes the group being examined in the research.

data set size

Highlights the quantity of data collected.

number of observations

Focuses on the individual data points within the study.

experimental group size

Specifically refers to the size of the group being tested in an experiment.

survey respondents

Highlights the number of individuals who completed a questionnaire

cohort size

Implies a group of subjects sharing a defining characteristic.

statistical population size

Refers to the theoretical number in a given statistical study.

group size

A general term indicating the number of people or items in a group.

respondent pool

Highlights the group of people that respond to a specific survey or questionnaire

FAQs

Why is "sample size" important in research?

The "sample size" affects the statistical power and reliability of a study. A larger "sample size" generally increases the likelihood of detecting a real effect and reduces the risk of false conclusions.

How do I determine the appropriate "sample size" for my study?

Determining the right "sample size" depends on factors like the desired statistical power, expected effect size, and variability in the population. Consult with a statistician or use "sample size calculators" to ensure your study is adequately powered.

What does it mean when a study has a small "sample size"?

A small "sample size" means the study included a limited number of participants or observations. This can make it difficult to generalize findings to the broader population and may increase the risk of a Type II error (failing to detect a real effect).

Are there alternatives to increasing "sample size"?

While increasing the "sample size" is often the most direct way to improve statistical power, other strategies include using more precise measurement tools, reducing variability in the study population, or employing more powerful statistical analyses. However, these should be considered carefully in consultation with a statistician.