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About Confidence Intervals
Margin of error and the interval around a mean.
Sample design decides whether a result can be trusted as much as the arithmetic does: a random sample of a thousand people can describe a country, while a large voluntary response sample describes only itself. Effect size and confidence interval together say more than a p-value alone.
Statistics in American teaching and research is organised around the normal curve: standard deviation measures spread, a z-score expresses a value in units of it, and a p-value below 0.05 is the conventional line for calling a result significant. Confidence intervals widen quickly as samples shrink, and a result is only as good as the sample it came from.
A confidence interval gives the range in which the true value plausibly sits, and a 95% interval means the method captures the truth in 95% of repeated samples. Wider intervals come from smaller samples and more variable data, not from a weaker finding.
The 0.05 threshold is a convention, not a law of nature: it means a result this extreme would arise by chance about one time in twenty if there were no real effect.
Sampling error and bias are different problems: a larger sample shrinks the first and does nothing about the second. A confidence interval describes the range the true value plausibly occupies, not the spread of the data.
Confidence Interval Calculator sits with the rest of the library's maths work, and it rests on the same statistical arithmetic the way a textbook or an exam paper does. Its answer is only as good as the assumptions standing behind it.
The quantities it works from — sample mean, standard deviation, sample size and confidence level — are the ones a real statistical question actually turns on, and each carries a convention of its own, set out below.
Mathematics is the one subject here that depends on nothing local: the same theorem holds in Ohio and Osaka, and the same fraction carries the same value everywhere. What changes is how results are written — American schooling teaches decimal notation and long division, and business arithmetic runs on fractions of a dollar.
Precision and rounding are where most practical errors creep in.