# Understanding  Sample Size

As a concept in research, sample size refers to the number of individuals or units that have been selected from a population for study. Determining an appropriate sample size is crucial since it can impact the reliability, validity, and generalizability of findings. In this post, we will explore what sample size is and answer some of the most common questions people have about it.

## What is Sample Size?

Sample size refers to the number of participants or data points in a research study. It’s important because it plays a significant role in determining the accuracy and representativeness of research results. The bigger the sample size, the higher is its representativeness.

## Why is Sample Size Important?

Sample size is important because it impacts the reliability and validity of research findings. A small sample size may not be representative of a larger population and can lead to results that are not statistically significant or generalizable. A larger sample size typically leads to more accurate results.

## How Do You Determine Sample Size?

There are different ways to determine sample size depending on your study design and research question. Power Analysis is one way to determine sample size through calculating statistical power (the probability of detecting an effect if there actually exists one) based on reasonable assumptions.

## What is Statistical Significance?

Statistical significance means that the results observed in a study are unlikely due to chance alone. Statistical significance is typically determined by p-value (probability value) being less than 0.05; however, statistical significance alone cannot tell us about practical significance or effect sizes.

## What is Effect Size?

Effect size describes how large an effect exists in your data beyond what would be expected by chance alone (or null hypothesis). There are varying measures for effect sizes, including Cohen's d, Omega squared and Pearson's r; which measure standardized magnitude of differences between two groups or correlations between two variables.

## What Is Random Sampling?

Random sampling is a method of selecting study participants from a larger population where each participant has an equal chance of being selected. This type of sampling eliminates bias and is essential in ensuring the representativeness of the sample.

## How Does Sample Size Affect Research?

Sample size affects research by influencing the reliability, validity, and generalizability of findings. A sample that is too small may lead to results that are not statistically significant or generalizable. A larger sample size typically leads to more accurate and reliable results.

## Conclusion

Sample size matters and is an essential component when conducting research. The larger the sample, the more representative it will be of the larger population. Careful consideration should be given to determining an appropriate sample size through power analysis and random sampling.

### References

• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). New York: Lawrence Erlbaum Associates.
• Kirk, R. E. (1996). Practical significance: A concept whose time has come ? Educational and Psychological Measurement, 56(5), 746–759.
• Creswell, J.W., & Creswell, J.D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches (5th Ed.) Los Angeles: Sage.
• Kline, R. B. (2013). Beyond significance testing: Reforming data analysis methods in behavioral research (2nd Edition.). Washington, DC: American Psychological Association.
• Sullivan G.M., Feinn R. (2012) Using Effect Size—or Why the P Value Is Not Enough J Grad Med Educ;4(3): 279–282.