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Ejecución de la prueba. La hipótesis nula (H 0 ) para la prueba es que los datos provienen de una distribución normal . La hipótesis alternativa (H 1 ) es que los datos no provienen de una distribución normal. La prueba asume que tiene una muestra aleatoria.
Se utiliza para probar la hipótesis nula de que los datos provienen de una población con distribución normal , cuando la hipótesis nula no especifica qué distribución normal; es decir, no especifica el valor esperado y la varianza de la distribución.
The Lilliefors test is a test for normality. It is an improvement on the Kolomogorov-Smirnov (K-S) test — correcting the K-S for small values at the tails of probability distributions — and is therefore sometimes called the K-S D test.
In statistics, the Lilliefors test is a normality test based on the Kolmogorov–Smirnov test. It is used to test the null hypothesis that data come from a normally distributed population, when the null hypothesis does not specify which normal distribution; i.e., it does not specify the expected value and variance of the distribution ...
The Kolmogorov-Smirnov Test (also known as the Lilliefors Test) compares the empirical cumulative distribution function of sample data with the distribution expected if the data were normal.
LCRIT(n, α, tails, interp) = the critical value of the Lilliefors test for a sample of size n, for the given value of alpha (default .05) and tails = 1 (one tail) or 2 (two tails, default) based on the Lilliefors Test Table. If interp = TRUE (default) the recommended interpolation is used; otherwise linear interpolation is used.
Among the many procedures used to test this as-sumption, one of the most well-known is a modification of the Kolomogorov-Smirnov test of goodness of fit, generally referred to as the Lilliefors test for normality (or Lilliefors test, for short).
