text: The<unk> , named after Theodore Wilbur<unk> , Jr. (1918–?) and Donald A.<unk>  (1915–?), who invented it in 1952, is one of the most powerful statistics for detecting most departures from normality. It may be used with small sample sizes n ≤ 25.  Very large sample sizes may reject the assumption of normality with only slight imperfections, but industrial data with sample sizes of 200 and more have passed the<unk> .[citation needed]
  The<unk>  assesses whether a sample comes from a specified distribution.  The formula for the test statistic 
  A
  {\displaystyle A}
   to assess if data 
  {
  Y
  1
  <
  ⋯
  <
  Y
  N
  }
  {\displaystyle \{Y_{1}<\cdots <Y_{N}\}}
   (note that the data must be put in order) comes from a distribution with cumulative distribution function (CDF) 
  F
  {\displaystyle F}
   is
  where
  The test statistic can then be compared against the critical values of the theoretical distribution (dependent on which 
  F
  {\displaystyle F}
   is used) to determine the P-value.
  The<unk>  for normality is a distance or empirical distribution function (EDF) test. It is based upon the concept that when given a hypothesized underlying distribution, the data can be transformed to a uniform distribution. The transformed sample data can be then tested for uniformity with a distance test (Shapiro 1980).
  In comparisons of power, Stephens (1974) found 
  A
  2
  {\displaystyle A^{2}}
   to be one of the best EDF statistics for detecting most departures from normality. The only statistic close was the 
  W
  2
  {\displaystyle W^{2}}
   (Cramér von-Mises test) statistic.
option_0: Anderson-Darling test
option_1: Patrick's test
option_2: Breusch-Pagan test
option_3: Ascheim-Zondek test
option_4: Tine test
option_5: Schober's test
option_6: Posterior drawer test
option_7: Ottawa ankle rules