In simple terms An estimate built from a sample is never exact. A confidence interval is the range of values that is likely to contain the quantity you were trying to measure. The US Census Bureau describes it as the uncertainty surrounding an estimate. A wide interval is a warning. A narrow one is a
A confidence interval is a range around an estimate that is likely to contain the true value, given the method used to produce it.
In simple terms A survey asks some people, not everyone. Ask a different sample and the answer would come out a little differently. The margin of error puts a size on that wobble. It does not say the survey is wrong. It says how precise the number is. How it works Sampling error is the
A margin of error shows how far a survey estimate could sit from the true population value simply because only a sample was measured.
In simple terms A meta-analysis pools the results of several studies that asked the same question. It treats them as one larger body of evidence. The output is a single summary figure, reported with a confidence interval that shows how much uncertainty is left. How it works A meta-analysis normally sits inside a systematic review.
A meta-analysis statistically combines the results of two or more separate studies to produce a single, more precise estimate of an effect.
In simple terms Every survey misses people. Nonresponse bias is what happens when the people it misses differ from the people it reaches. If the difference has nothing to do with the question being asked, the estimate can still be sound. If it does, the number is wrong, and a larger sample will not rescue
Nonresponse bias is the error that appears when the people who answer a survey differ from those who do not in ways that matter.
In simple terms A p-value is a number that says how surprising your data would be if nothing were really going on. The American Statistical Association puts it carefully: a p-value can indicate how incompatible the data are with a specified statistical model. How it works Researchers start from a null hypothesis, the assumption of
A p-value measures how incompatible observed data are with a specified statistical model, usually one that assumes no real effect.
In simple terms A finding replicates when a different team asks the same question, gathers its own data, and reaches a consistent answer. The replication crisis is the concern that this happens less often than scientists assumed. It is an argument about how much confidence a single published result deserves. It is not a claim
The replication crisis is the finding that many published scientific results do not hold up when other researchers repeat the study with new data.
In simple terms Statistical significance is a check on whether a pattern in data could plausibly be a fluke. Researchers start from a null hypothesis, the assumption that there is no real effect. They then ask how surprising their data would be if that assumption were true. How it works The answer arrives as a
A result is statistically significant when it would be unlikely to appear by chance alone if the effect being tested were truly absent.