The goodness of fit of a statistical model describes how well it fits a set of observations. Measures of goodness of fit typically summarize the discrepancy between observed values and the values expected under the model in question. Such measures can be used in statistical hypothesis testing, e.g. to test for normality of residuals, to test whether two samples are drawn from identical distributions (see Kolmogorov–Smirnov test), or whether outcome frequencies follow a specified distribution (see Pearson's chi-squared test). In the analysis of variance, one of the components into which the variance is partitioned may be a lack-of-fit sum of squares.
Other articles related to "goodness of fit, fit":
... Consultation of the chi-squared distribution for 1 degree of freedom shows that the probability of observing this difference (or a more extreme difference than this) if men and women are equally numerous in the population is approximately 0.23 ... This probability is higher than conventional criteria for statistical significance (0.001–0.05), so normally we would not reject the null hypothesis that the number of men in the population is the same as the number of women (i.e ...
... is also a form of Moran or Moran-Darling statistic, M(θ), which can be used to test goodness of fit ...
... ratio test statistic is a measure of the goodness of fit of a model, judged by whether an expanded form of the model provides a substantially improved fit ...
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—William Butler Yeats (18651939)