otantavirhe

Otantavirhe, or sampling error, is the discrepancy between a statistic calculated from a sample and the true parameter of the whole population that the sample represents. It arises because the sample is only a subset and not the entire population, and therefore it reflects random variation rather than the exact population values. Otantavirhe is inherent to probabilistic sampling and exists even when the sampling process is conducted correctly and without mistakes.

The magnitude of otantavirhe depends on several factors, including how variable the population is, the sample size, and the sampling design. In general, larger samples and more efficient designs reduce the expected size of the sampling error. It is important to distinguish otantavirhe from bias and other non-sampling errors; otantavirhe is the random component of error, while bias is a systematic deviation that can persist irrespective of sample size.

In practice, otantavirhe is often quantified by the standard error and communicated through margins of error or confidence intervals. For example, a poll might report a margin of error that indicates the range within which the true population value is likely to fall, given the observed sample statistic. Formulas used to approximate standard errors vary: for proportions, SE ≈ sqrt(p(1−p)/n); for means, SE ≈ s/√n, where n is sample size, p is the sample proportion, and s is the sample standard deviation.

Reducing otantavirhe involves careful sampling design, such as random or stratified sampling, increasing sample size, and applying weighting or design-based inference. It is complemented by addressing non-sampling errors (measurement, nonresponse, data processing) to ensure overall data quality.