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A set of 100 randomly generated points displayed on a scatter graph. Examining the points, it is easy to identify apparent patterns. In particular, rather than spreading out evenly, it is not uncommon for random data points to form clusters, giving the (false) impression of "hot spots" created by some underlying cause.
Simpson's paradox is a phenomenon in probability and statistics in which a trend appears in several groups of data but disappears or reverses when the groups are combined. This result is often encountered in social-science and medical-science statistics, [ 1 ] [ 2 ] [ 3 ] and is particularly problematic when frequency data are unduly given ...
Matching is a statistical technique that evaluates the effect of a treatment by comparing the treated and the non-treated units in an observational study or quasi-experiment (i.e. when the treatment is not randomly assigned).
Overmatching, matching for an apparent confounder that actually is a result of the exposure [clarification needed]. The control group becomes more similar to the cases in regard to exposure than does the general population. Survivorship bias, in which only "surviving" subjects are selected, ignoring those that fell out of view. For example ...
To promote a neutral (useless) product, a company must find or conduct, for example, 40 studies with a confidence level of 95%. If the product is useless, this would produce one study showing the product was beneficial, one study showing it was harmful, and thirty-eight inconclusive studies (38 is 95% of 40).
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The mean signed difference is derived from a set of n pairs, (^,), where ^ is an estimate of the parameter in a case where it is known that =. In many applications, all the quantities θ i {\displaystyle \theta _{i}} will share a common value.