overall sample size

USAGE SUMMARY

The phrase "overall sample size" is correct and usable in written English.
It can be used when discussing the total number of observations or participants included in a study or research project. Example: "The overall sample size for the survey was 500 participants, which provided a robust dataset for analysis."

✓ Grammatically correct

Science

News & Media

Formal & Business

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

The overall sample size was 38,974.

The Guardian - Opinion

"While the study design may not have been optimal," Dr. Morris said, "the differences are striking given the relatively large overall sample size".

The New York Times

Overall sample size was N = 620.

Evolution: Education and Outreach

Overall sample size comprises of 215 respondents.

Journal of Innovation and Entrepreneurship

Most firefighters took part in more than one trial because of the limited overall sample size.

Human-centric Computing and Information Sciences

The MLR estimator was used and the data was weighted with individual case weights scaled to the overall sample size.

Large-scale Assessments in Education

Twelve studies fulfilled the inclusion criteria of which six were unpublished (overall sample size = 11.159 children) (Table 1).

Sports Medicine - Open

Then weights are finally normalized to the original overall sample size from 2003 to 2010: WEIGHT = WEIGHT2/mean WEIGHT2).

The Journal of Chinese Sociology

An extensive literature search detected 174 samples from 122 studies, which resulted in 786 test outcomes and an overall sample size of 153,185.

Intelligence

On the other hand, small gain antennas will have lesser anomalies in the data but at the cost of lower overall sample size of pedestrian and cyclist's data.

Transportation Research Part C: Emerging Technologies

A total of 13,098 men were selected for participation with a final overall sample size of 14,499 male and female inmates.

Health & Justice

Expert writing Tips

Best practice

When reporting the "overall sample size", always clarify whether this figure represents the initial target, the achieved sample, or the effective sample size after accounting for missing data or exclusions. This ensures transparency and avoids misinterpretations.

Common error

Avoid stating the initially planned sample size as the "overall sample size" if the final dataset contains fewer observations due to attrition or other factors. Always report the actual number of observations used in the analysis to prevent misleading interpretations of the study's statistical power.

Linguistic Context

The phrase "overall sample size" functions as a noun phrase that identifies the total number of subjects or observations included in a study or analysis. This is supported by Ludwig AI's assessment confirming its correctness and usability.

Expression frequency: Very common

Frequent in

Science

75%

News & Media

15%

Formal & Business

10%

Less common in

Encyclopedias

0%

Wiki

0%

Reference

0%

Ludwig's WRAP-UP

The phrase "overall sample size" is a common and grammatically correct way to refer to the total number of observations in a study. Ludwig confirms its correctness and usability. It is most frequently used in scientific and academic contexts, as well as in news reports discussing research findings. When using this phrase, it is important to clearly indicate whether it refers to the initial target sample, the achieved sample, or the effective sample size after exclusions. Related phrases include "total sample size" and "aggregate sample size". A clear understanding of the "overall sample size" is essential for interpreting the statistical power and generalizability of research results.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

total sample size

Replaces "overall" with "total", emphasizing the completeness of the sample.

aggregate sample size

Uses "aggregate" to highlight the combined nature of the sample.

entire sample size

Substitutes "overall" with "entire", stressing the wholeness of the sample.

complete sample size

Similar to 'entire sample size', focuses on the completeness of the sample.

combined sample size

Highlights that the sample size might be a result of combining multiple smaller samples.

final sample size

Suggests this is the sample size after accounting for attrition or adjustments.

net sample size

Indicates the sample size after accounting for any reductions or exclusions.

study population size

Focuses on the population from which the sample is drawn.

research sample size

Emphasizes the sample's role in a research context.

observed sample size

Highlights the size of the sample that was actually observed or measured.

FAQs

How is "overall sample size" determined in research?

The "overall sample size" is determined based on factors such as the desired statistical power, expected effect size, and acceptable margin of error. Researchers use power analysis to calculate the minimum sample size needed to detect meaningful differences or relationships in their data.

What does a large "overall sample size" indicate?

A large "overall sample size" generally increases the statistical power of a study, making it more likely to detect true effects and reduce the risk of false negatives. However, a large sample size does not guarantee the validity of the findings if the sample is not representative or the study design is flawed.

What are the limitations of a small "overall sample size"?

A small "overall sample size" can limit the statistical power of a study, making it difficult to detect true effects and increasing the risk of false negatives. It may also reduce the generalizability of the findings to the broader population. Increasing your sample size can reduce the likelihood of this.

How does the "overall sample size" affect the interpretation of results?

The "overall sample size" is crucial for interpreting research results. A statistically significant finding in a study with a small sample size may not be as robust or generalizable as a similar finding in a study with a larger sample size. Researchers should always consider the sample size when evaluating the strength and reliability of their conclusions.