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Sample Design - Part 1. Sample Size - or why just over 1000 responses is enough to generalize findings to millions of people!
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In this episode of the Total Survey Design Podcast, we explore the fascinating concept of sample size and its impact on survey accuracy. Discover why just about 1000 responses can represent the views of millions, debunking common misconceptions about survey sampling. We'll dive into the key parameters for calculating sample size, such as margin of error, confidence level, predicted variance, and target population size. Learn how a small, well-chosen sample can provide precise and reliable data, making large-scale surveys both practical and cost-effective. Tune in to understand the math behind the magic!
Here's a general formula commonly used for calculating sample size for simple random sampling:
𝑛=𝑍^2*𝑃*(1−𝑃)/𝐸^2
Where:
𝑛 = required sample size
𝑍 = Z-value (the number of standard deviations from the mean corresponding to the desired confidence level)
For a 95% confidence level, 𝑍≈1.96
𝑃 = estimated proportion of the population (if unknown, 𝑃 is often assumed to be 0.5 to maximize the sample size)
𝐸 = margin of error (the desired level of precision, expressed as a decimal)
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