Testing for Normality, Tests for Normality, Graphical Methods YouTube Lecture Handouts

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Testing for Normality

Testing for Normality

Population vs sample

How we test normality

Graphical Data
  • Difference between theoretical distribution and actual data – we require tests of normality
  • When is non-normality a problem?
  • Normality can be a problem when the sample size is small (< 50) .
  • Highly skewed data create problems.
  • Highly leptokurtic data are problematic, but not as much as skewed data.
  • Normality becomes a serious concern when there is “activity” in the tails of the data set.
  • Outliers are a problem. (Test used are Grubb՚s test and Dixon test)
  • “Clumps” of data in the tails are worse.
  • Final Words Concerning Normality Testing:
  • Since it is a test, state a null and alternate hypothesis.
  • If you perform a normality test, do not ignore the results.
  • If the data are not normal, use non-parametric tests.
  • If the data are normal, use parametric tests.
  • If you have groups of data, you MUST test each group for normality.

Tests for Normality

Tests for Normality
  • Statistical tests for normality are more precise since actual probabilities are calculated.
  • Tests for normality calculate the probability that the sample was drawn from a normal population.

The hypotheses used are:

  • Ho: The sample data are not significantly different from a normal population.
  • Ha: The sample data are significantly different from a normal population.

When testing for normality:

  • Probabilities indicate that the data are normal.
  • Probabilities indicate that the data are NOT normal
  • SPSS Normality Tests - Kolmogorov-Smirnov and Shapiro-Wilk.
  • PAST Normality Tests - Shapiro-Wilk, Anderson-Darling, Lilliefors, Jarque-Bera.

Q-Q Plots

Q-Q Plots Normaly Distributed Data

Q-Q plots display the observed values against normally distributed data (represented by the line) .

Normally distributed data fall along the line

Graphical Methods

Graphical Methods

Graphical methods are typically not very useful when the sample size is small. This is a histogram of the last example. These data do not ‘look’ normal, but they are not statistically different from normal

W/S Test

Range Constant SD Change
Graphical and Statisticed

W/S Test for Normality

  • A simple test that requires only the sample standard deviation and the data range.
  • Should not be confused with the Shapiro-Wilk test.
  • Based on the q statistic, which is the ‘studentized’ (meaning t distribution) range, or the range expressed in standard deviation units.
  • Where q is the test statistic, w is the range of the data and s is the standard deviation.
  • The test statistic q (Kanji 1994, table 14) is often reported as u in the literature.

Jarque-Bera Test

Normality is one of the assumptions for many statistical tests, like the t test or F test; the Jarque-Bera test is usually run before one of these tests to confirm normality. It is usually used for large data sets, because other normality tests are not reliable when n is large

  • Where: n is the sample size, S is sample skewness
  • K is sample kurtosis
  • In general, a large J-B value indicates that errors are not normally distributed.
  • For sample sizes of 2,000 or larger, this test statistic is compared to a chi-squared distribution with 2 degrees of freedom (normality is rejected if the test statistic is greater than the chi-squared value) .
  • The chi-square approximation requires large sample sizes to be accurate. For sample sizes less than 2,000, the critical value is determined via simulation.


The test gives you a W value; small values indicate your sample is not normally distributed


  • are the ordered random sample values
  • are constants generated from the covariances, variances and means of the sample (size n) from a normally distributed sample.
  • The test has limitations, most importantly that the test has a bias by sample size. The larger the sample, the more likely you՚ll get a statistically significant result.
  • Univariate continuous data
  • Numerator is slope of observed data vs expected normal values
  • If is true then W should be 1.
  • Highly sensitive and use graphical method to assess t-test assumptions

Kolmogorov-Smirnov (K-S Test)

It compares the observed versus the expected cumulative relative frequencies

Kolmogorov-Smirnov test uses the maximal absolute difference between these curves as its test statistic denoted by D.

  • It only applies to continuous distributions.
  • It tends to be more sensitive near the center of the distribution than at the tails. Determined by stimulation.
  • The Kolmogorov-Smirnov (K-S) test is based on the empirical distribution function (ECDF) . Given N ordered data points , the ECDF is defined as


  • where n (i) is the number of points less than and the are ordered from smallest to largest value. This is a step function that increases by at the value of each ordered data point.
  • Calculated value critical value (acceptance criteria) default is 0.565 as critical value

D՚Agostino Test

A very powerful test for departures from normality.

Based on the D statistic, which gives an upper and lower critical value.

Where D is the test statistic, SS is the sum of squares of the data and n is the sample size, and I is the order or rank of observation x. The df for this test is n (sample size) .

First, the data are ordered from smallest to largest or largest to smallest

is middle term of dataset

is observations՚ distance from middle

Notice that as the sample size increases, the probabilities decrease. In other words, it gets harder to meet the normality assumption as the sample size increases since even small departures from normality are detected.

Statistical Tests Highlights

W/S or studentized range (q) :

  • Simple, very good for symmetrical distributions and short tails.
  • Very bad with asymmetry.

Shapiro Wilk (W) :

  • Powerful omnibus test. Not good with small samples or discrete data.
  • Good power with symmetrical, short, and long tails. Good with asymmetry.

Jarque-Bera (JB) :

  • Good with symmetric and long-tailed distributions.
  • Less powerful with asymmetry, and poor power with bimodal data.

D՚Agostino (D or Y) :

  • Good with symmetric and very good with long-tailed distributions.
  • Less powerful with asymmetry.

Anderson-Darling (A) :

  • Similar in power to Shapiro-Wilk but has less power with asymmetry.
  • Works well with discrete data.

Distance tests (Kolmogorov-Smirnov, Lillifors, and Chi2) :

  • All tend to have lower power. Data have to be very non-normal to reject Ho.
  • These tests can outperform other tests when using discrete or grouped data.
  • Several goodness-of-fit tests, such as the Anderson-Darling test and the Cramer Von-Mises test, are refinements of the K-S test. As these refined tests are generally considered to be more powerful than the original K-S test, many analysts prefer them. In addition, the advantage for the K-S test of having the critical values be independent of the underlying distribution is not as much of an advantage as first appears.

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