Key point: A calculator cannot rescue a biased sampling process.
Sample size affects precision, but sampling quality affects who the findings represent. Learn how to evaluate both before data analysis.
Sample size and sampling answer different questions
Sample size helps determine the precision and power of an analysis under stated assumptions. Sampling strategy concerns how participants enter the study and whether the sample can represent the intended population.
Define the population before recruitment
Specify who the findings are intended to represent, the inclusion and exclusion criteria, the sampling frame and the recruitment method. A large online sample may still exclude important groups.
Examine nonresponse
Researchers should consider who did not participate and why. If nonresponse is connected to the outcome, the final sample may provide a distorted picture even when the number of responses is high.
Write a defensible limitation
Explain what the sampling method permits you to claim and what it does not. Avoid implying population wide generalisability when the recruitment process cannot support it.
How Speedrise Research can help
We provide research writing guidance, academic editing, quantitative and qualitative analytical assistance, presentation development and responsible manuscript revision for undergraduate, master’s and doctoral researchers worldwide.
Need support with your research?
Send your academic level, research stage, deadline and required support through WhatsApp. We will review the scope and recommend the most appropriate service.
About the author
Kazeem Afen
Kazeem Afen is an academic research consultant at Speedrise Research. He supports undergraduate, master’s and doctoral researchers with research development, academic editing, quantitative and qualitative analysis, presentations and responsible manuscript revision.