Calculating the required sample size and statistical power
Correctly calculating the sample size is one of the most important steps in planning quantitative research. Sample size directly affects the validity of the findings, the ability to generalize to a broad population, and the power of the statistical tests. A sample that is not well planned may lead to non-significant findings, or alternatively to a waste of unnecessary resources. It is therefore important to perform the calculation based on clear criteria agreed in advance, according to the research question and the nature of the data.
Margin of error and confidence interval
Two of the central measures in calculating sample size are the margin of error and the confidence interval. The margin of error represents the limit within which we are willing to be wrong in estimating a population parameter (for example, ±5%). The confidence interval expresses our level of certainty in the estimate – we usually use 95% or 99%. The smaller the margin of error we want, or the higher the confidence level, the larger the sample we will need. These are decisions that require balancing the study’s requirements against limits of time, budget and access to participants.
Statistical power and effect size
Calculating statistical power is intended to ensure a high chance of detecting a real effect when it actually exists in the population. Accepted statistical power is at least 80%, meaning an 80% chance of detecting statistical significance when it exists, that is, when there are differences or relationships at the population level too. This calculation depends on factors such as the expected effect size, the desired significance level (p-value), and whether the test is one-tailed or two-tailed. Inaccurate use of these measures may lead to a study that cannot answer its central questions – even if data collection was done properly.
Whether you are at the stage of planning a survey, a clinical trial, or an academic quantitative study – we help you calculate the sample size accurately and in line with your research goals. Our team uses professional tools and advanced statistical methods, taking into account accepted research standards. Our goal is to help you plan a valid, economical study with a high chance of leading to significant results.