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We know how to calculate the necessary sample size because the Central Limit Theorem tells us that the sampling distribution of the mean (or proportion) becomes
by firesteelrain 1y ago
We know how to calculate the necessary sample size because the Central Limit Theorem tells us that the sampling distribution of the mean (or proportion) becomes approximately normal as the sample size grows. This justifies using Z-scores from the normal distribution to derive confidence intervals - and from there, solve for the sample size needed to achieve a desired margin of error and confidence level.
Article used a Python library without really understanding the reason or science behind the result. Knowing this can help when you read an article or watch a news report where they quote a study that says “a study of 300 people…” well why 300 people? You can reasonably assume that the researchers used the CLT.
- Ntrails 1y agoThe real world constraints of time and money are non trivially involved in sample size decisions. CLT may be invoked but I do not give the benefit of the doubt to studies being announced at this point
- firesteelrain 1y agoYou are right that time, cost, and feasibility often drive sample size decisions more than statistical ideals. My point was just that when researchers cite a specific number (like 300), there’s often a statistical basis tied to confidence levels and margin of error. Skepticism is healthy since not all studies follow best practices