What Is Sample Size?

Sample size is the number of observations included in a study or experiment. It is the number of individuals, items, or data points selected from a larger population to represent it statistically. The sample size is a crucial consideration in research because it directly impacts the reliability and extent to which you can generalise those findings to the larger population.

A larger sample size could enhance the precision of estimates, leading to a narrower margin of error. In other words, the results from a larger sample will likely be closer to the true population parameter. A larger sample size can also increase the power of a statistical test. This means that with a larger sample, you are less likely to find results that are not true.

However, a sample size that is too large can save money and time. A good study will find the most accurate results with the fewest subjects.

How do you determine sample size?

Determining the appropriate sample size for a study will usually involve considering the purpose of the study, population size, risk of committing an error, and available resources. When determining the appropriate sample size, consider factors such as the following:

Your study design

Different types of studies might require different sample sizes. For example, a study aiming to understand a rare disease might need a larger sample size to ensure it captures enough cases for analysis. Whether you choose to conduct an observational, cohort, case-control, or experimental study will affect the sample size you need.

Your population

You will need a higher sampling ratio with a smaller population than a larger one. If you conduct a survey, you may also need to factor in your estimated response rate to ensure you sample enough people to get the required responses. If you have a high degree of variability in your target population, you may also need to increase your sample size to increase the likelihood that your sample represents the population of interest.

Your statistical methods

You can use several statistical methods, such as power analysis, to calculate an appropriate sample size. This method considers the effect size, significance level, and power to calculate the sample size. Essentially, you will need to determine what level of confidence you want to have in your results and how much error you are willing to accept.

Your available resources

Lastly, practical considerations like time, money, and subject availability can affect the chosen sample size. While a larger sample size may offer more accurate results, it can also require more resources to collect and analyse.

Related terms

• Margin of error

• Unstructured data

• Statistical modelling 

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Sample size refers to the number of participants in a study and directly impacts the reliability of the results for the whole population. A larger sample size generally leads to more precise estimates and a stronger ability to detect true effects.

Understanding sample size and significance can help you perform many analytical tasks. The appropriate sample size is pivotal for the validity and reliability of the study's findings and ensuring that the resources used in conducting the research are efficiently utilised. Consider the Google Data Analytics Professional Certificate on Coursera to expand your analytical skills and build a job-ready portfolio.