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In survey research, a sample of about $$1,000$$ individuals is typically considered adequate to make accurate estimates, regardless of whether the broader population consists of a hundred thousand or a hundred million people. This is because a $$95\%$$ confidence interval depends only on the size of the sample itself, not on the size of the population. Furthermore, while increasing the sample size continues to shrink the confidence interval—meaning the sample statistic is likely closer to the true population value—it does so at a progressively slower rate. Consequently, the minor gains in statistical confidence achieved by surveying significantly more than $$1,000$$ people are generally not considered worth the additional time and financial cost.

Sample Size and Population Size

In survey research, why are the minor statistical gains from surveying significantly more than 1,000 people generally not considered worth the additional time and financial cost?

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