Calculate the required sample size to obtain statistically significant results when conducting surveys or research. Determine appropriate sample size by setting confidence level and margin of error.
Selecting an appropriate sample size is crucial for obtaining statistically significant research results. Too small a sample yields unreliable results, while too large wastes resources and time. This calculator provides optimal sample size considering confidence level and margin of error.
Confidence level represents the probability that research results represent the population, typically 95%. Margin of error indicates how much results may differ from actual values, usually ±5%. Higher confidence levels and smaller margins require larger samples.
For small populations, sample size increases proportionally with population size. However, for sufficiently large populations (tens of thousands or more), sample size converges to a constant value nearly independent of population size. For large populations, approximately 384 samples suffice.
Use this calculator when designing various surveys including marketing research, opinion polls, and academic studies. Determine feasible sample sizes by adjusting confidence level and margin of error considering budget and time constraints. Plan for actual contact numbers considering response rates.
Expected proportion is an estimate of the percentage having the characteristic being studied. For example, when surveying product preference, estimate this proportion based on prior research or experience. Using 50% when uncertain yields the most conservative sample size.
The calculated sample size is a theoretical minimum. In actual research, secure 20-30% more samples to account for non-responses and insincere responses. If planning subgroup analysis, increase overall sample size to ensure sufficient samples in each group.