1. Goodness-of-Fit Tests
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2. Hypotheses
The null hypothesis in a GOF test is that the sample came from a specific population (e.g., normal, uniform, Poisson, binomial). Does the sample contradict the hypothesis?

3. Parameters
The parameters of the proposed distribution (e.g., its mean) might be specified a priori (e.g., from a non-sample benchmark) but more commonly they must be estimated from the sample.

4. Normality Test
The most common test is for normality. MINITAB and other computer packages offer various normality tests.

5. Checking Normality We can: Inspect the histogram.
Check the Empirical Rule:
Compare the sample mean and median (symmetry).
Check the skewness and kurtosis statistics.
Do a chi-square test for normality.
Inspect the probability plot.
Inspect the ECDF plot.
Do ECDF-based tests (Anderson-Darling, Kolmogorov-Smirnov, etc).

6. Histogram
We have a sample of Kentucky Derby winning times (in seconds) from 1950 through Are the data normally distributed?
Histogram appears symmetric, though perhaps with too much concentration in the middle.
Source David P. Doane, Kieran Mathieson, and Ronald L. Tracy, Visual Statistics 2.0 (Irwin/McGraw-Hill, 2001).

7. Descriptive Statistics
We have a sample of Kentucky Derby winning times (in seconds) from 1950 through Are the data normally distributed?
The mean and median (Quartile 2) are identical. For the fitted data, we calculate the likely range, and find that the sample range is slightly less than expected. Data are symmetric (skewness near 0) but somewhat leptokurtic (kurtosis above 3).
Source David P. Doane, Kieran Mathieson, and Ronald L. Tracy, Visual Statistics 2.0 (Irwin/McGraw-Hill, 2001).

8. Chi-Square Test Test statistic where
Hypotheses:
H0: Winning Derby times are normal
H1: Winning Derby times are not normal
where
fj = the observed frequency in group j
ej = the expected frequency in group j if H0 is true
For c classes we use d.f. = c – 1 – m where m is the number of parameters estimated to fit the distribution
Example: m = 2 for a normal if m and s are estimated)

9. Chi-Square Test Hypotheses: H0: Winning Derby times are normal
H1: Winning Derby times are not normal
Since the p-value is 0.009, the chi-square test statistic is significant at a = 0.01, leading to rejection of the hypothesis of normality. Most of the problem is in the middle category, as we noted in the histogram.
The normal end categories are open-ended. We prefer that all expected frequencies be at least 5.
Source David P. Doane, Kieran Mathieson, and Ronald L. Tracy, Visual Statistics 2.0 (Irwin/McGraw-Hill, 2001).

10. ECDF Plot
An Empirical Cumulative Distribution function plots the cumulative frequency against the sample values. Sometimes a fitted normal distribution is displayed. This plot is always done by a computer, not by hand.
This ECDF plot follows approximately the S-shape that characterizes a normal distribution. This resemblance is enhanced by superimposing a fitted normal cumulative distribution
Source David P. Doane, Kieran Mathieson, and Ronald L. Tracy, Visual Statistics 2.0 (Irwin/McGraw-Hill, 2001).

11. Kolmogorov-Smirnov Test
The K-S test is based on the largest vertical distance from the fitted normal. Special tables are required if you want a p-value This test is always done by a computer, not by hand. It is less common than the chi-square or Anderson-Darling test.
The K-S statistic (D = 0.138) isn't significant at a = 0.20, so there isn't much evidence against H0. However, the K-S is not the most powerful test.
Hypotheses:
H0: Winning Derby times are normal
H1: Winning Derby times are not normal
Source David P. Doane, Kieran Mathieson, and Ronald L. Tracy, Visual Statistics 2.0 (Irwin/McGraw-Hill, 2001).

12. Probability Plot
If the data are normal, the normal probability plot should be a straight line. This plot is always done by a computer, not by hand.
Overall, the probability plot is fairly linear, although there are a few points at the ends and in the middle that are somewhat off the 45o line.
Source David P. Doane, Kieran Mathieson, and Ronald L. Tracy, Visual Statistics 2.0 (Irwin/McGraw-Hill, 2001).

13. Anderson-Darling Test
This result is from Minitab. The A-D test is always done by a computer, not by hand. The A-D test is powerful, but lacks the intuitive interpretation of a chi-square test.
Hypotheses:
H0: Winning Derby times are normal
H1: Winning Derby times are not normal
The A-D test statistic (0.708) yield a p-value of 0.061, so we could reject H0. at a = 0.10 but not at a = 0.05.
Copyright Notice Portions of MINITAB Statistical Software input and output contained in this document are printed with permission of Minitab, Inc. MINITABTM is a trademark of Minitab Inc. in the United States and other countries and is used herein with the owner's permission.

14. Other Tests
You can test for non-normal distributions (uniform, Poisson, etc) but those tests require a computer package designed for that purpose (e.g., Visual Statistics or Palisade's BestFitTM)

15. Conclusion: Data appear uniform.
Example: Uniform Test
Hypotheses:
H0: 3-digit Lottery winning numbers are uniform
H1: 3-digit Lottery winning numbers are not uniform
Conclusion: Data appear uniform.
Source David P. Doane, Kieran Mathieson, and Ronald L. Tracy, Visual Statistics 2.0 (Irwin/McGraw-Hill, 2001).

16. Summary Test Method Comments Eyeball method
Inspect the histogram and see if it looks like the hypothesized distribution.
Imprecise but easy. May suffice to rule out the proposed distribution.
Chi-square test
Form k categories, count the X values in each category, and use the chi-square test to compare the actual count with the expected count if X follows the hypothesized distribution.
Common. Must avoid small expected frequencies. Need special software (Excel, Minitab, Visual Statistics, BestFit).
Kolmogorov-Smirnov test
Find the greatest difference between the empirical cumulative distribution and the hypothesized distribution.
Easy to visualize. Need specialized software (Visual Statistics, BestFit).
Anderson-Darling test
Compare the empirical cumulative data distribution with the hypothesized distribution.
Widely used by researchers. Not intuitive. Need special software (Minitab, Visual Statistics, BestFit).