Determining Sample Size

Deciding on the number of participants to include in a survey is one of the most critical and often challenging steps in the research process. It is a decision that strikes a balance between the need for statistical precision and the practical limitations of a research project. A sample that is too small may fail to yield reliable results, making it impossible to draw meaningful conclusions about the population. Conversely, a sample that is excessively large wastes time, money, and resources, and can place an unnecessary burden on participants for only marginal gains in accuracy. The goal is not to get the largest sample possible, but to obtain a sample that is adequate and justifiable

Practical Considerations

Before delving into statistical theory, the choice of sample size is often shaped by real-world constraints. Researchers must be pragmatic and acknowledge the factors that can influence the scope of their study. These practical considerations are often the starting point for determining a feasible sample size

Budget and Resources

This is frequently the most significant limiting factor. Costs can include participant incentives, access to a survey panel, printing and postage for mail surveys, and the time of research staff. A larger sample size directly translates to higher costs

Time Constraints

Research projects operate on a timeline. The amount of time available to recruit participants, collect data, and analyze it can limit the potential sample size

Availability of the Target Population

If the study focuses on a very specific or hard-to-reach group (e.g., helicopter pilots, individuals with a rare medical condition), the pool of potential participants is naturally limited. In these cases, the goal may be to survey as many members of the population as possible, rather than a statistically calculated sample

Expected Response Rate

It is rare to get a 100% response rate. Researchers must estimate the likely response rate and recruit a larger initial sample to compensate. For example, if you need 400 completed surveys and you anticipate a 20% response rate, you will need to invite at least 2,000 people to participate

Analytical Complexity

The planned data analysis can also dictate sample size. If the researcher intends to analyze data from multiple subgroups (e.g., comparing responses by age, gender, and geographic region), each subgroup must have a sufficient number of participants to allow for meaningful comparison

An Introduction to Statistical Power

Beyond practicalities, determining sample size is a statistical exercise centered on the concept of power. Conceptually, statistical power is the probability that your study will detect an effect or a relationship that truly exists within the population. Think of it as the sensitivity of your research “instrument.” A study with low power is like using a blurry microscope—the thing you are looking for might be there, but you are unlikely to see it clearly. A high-powered study, on the other hand, uses a sharp, focused microscope, giving you a much better chance of finding what you are looking for, if it exists

A formal power analysis is a statistical calculation, but its underlying principles are essential for any researcher to understand. It involves a trade-off among four key components, where changing one affects the others

Sample Size

This is the most direct way a researcher can influence power. All else being equal, a larger sample size leads to higher statistical power. More data points provide a clearer, more stable picture of the population, making it easier to distinguish a true effect from random noise

Effect Size

This refers to the magnitude of the relationship or difference you expect to find. A “large” effect (like a massive difference in customer satisfaction between a brilliant product and a terrible one) is easy to detect and requires a smaller sample. A “small” or subtle effect (like a slight preference for one bottle design over another) is much harder to spot and requires a much larger sample and higher power to detect reliably

Significance Level (Alpha)

This is the threshold researchers set for claiming a result is “statistically significant,” typically set at p < .05. It represents the risk you are willing to take of making a “false positive” error—concluding there is an effect when one doesn’t actually exist. A more stringent (lower) alpha level requires more power, and therefore a larger sample size, to reach the threshold of significance

Variability in the Population

If the population is very homogenous and people’s opinions are clustered tightly together, a smaller sample can be representative. If the population is very diverse and opinions are all over the map (high variability), a larger sample is needed to capture that diversity and find a stable, reliable average

Ultimately, determining sample size is a blend of science and pragmatism. While online calculators can perform the power calculations, a researcher must first consider the practical constraints of their project and think critically about the expected effect size and the level of certainty required. The final sample size should be a deliberate choice that balances the need for robust, powerful results with the resources and time available