Sampling

Exam items rarely ask “what is sampling” — they hand you a scenario and make you name the bias or pick the sampling method. The classic trap is sampling error versus sampling bias: sampling error is the random gap between a statistic and the parameter, shrinks as n grows, and is not a mistake; bias is a systematic flaw that more data won’t fix. Know the named biases cold — survivorship bias (dead funds dropped from a database inflate returns), data-snooping/data-mining bias, look-ahead bias, and time-period bias. The central limit theorem is the workhorse: for n ≥ 30 the sampling distribution of the mean is approximately normal regardless of the population’s shape, which is what lets you build a confidence interval.

Distinguish stratified random sampling (divide into strata, sample within each — greater precision, used for bond-index replication) from cluster sampling (sample whole clusters — cheaper, usually less precise). Don’t confuse a sampling distribution with a probability distribution of one variable, and remember the standard error (of the mean) is not the standard deviation of the data.