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Nonparametric
Statistical Methods
Dr. Md Fazlul Haque
Associate Professor
Dept. of Zoology
University of Rajshahi
Definition
When the data is generated from process (model) that is
known except for finite number of unknown parameters
the model is called a parametric model.
Otherwise, the model is called a non-parametric model
The branch of inferential statistics devoted to
distribution-free tests is called nonparametric.
Distribution-free tests are statistical tests that do not
rely on any underlying assumptions about the
probability distribution of the sampled population.
For example
If you assume that your data has come from a
normal distribution with mean  and standard
deviation  (both unknown) then the data is
generated from process (model) that is known
except for two of parameters ( and  ).
The model is called a parametric model.
Models that do not assume normality (or some
other distribution with a finite no. of parameters)
are non-parametric
© 2011 Pearson Education, Inc
Scales of Measurement
• Nominal scale:A nominal scale is really a list of categories
to which objects can be classified.
• Ordinal scale:is a measurement scale that assigns values to
objects based on their ranking with respect to one another.
• Interval scale: one unit on the scale represents the same magnitude
on the trait or characteristic being measured across the whole range of
the scale. For example, if anxiety were measured on an interval scale,
then a difference between a score of 10 and a score of 11 would
represent the same difference in anxiety as would a difference
between a score of 50 and a score of 51.
• Ratio scale: Ratio scales are like interval scales except they
have true zero points.
© 2011 Pearson Education, Inc
Parametric Test Procedures
• Involve population parameters
— Example: population mean, standard deviations or
probabilities
• Require interval scale or ratio scale
— Whole numbers or fractions
— Example: height in inches (72, 60.5, 54.7)
• Have stringent assumptions
— Example: normal distribution
• Examples: z-test, t-test, F-test
© 2011 Pearson Education, Inc
Nonparametric Test
Procedures
• Do not involve population parameters
— Example: probability distributions, independence
• Data measured on any scale
— Ratio or interval
— Ordinal
 Example: good-better-best
— Nominal
 Example: male-female
• Example: Wilcoxon rank sum test
© 2011 Pearson Education, Inc
Advantages of
Nonparametric Tests
• Used with all scales
• Easier to compute
— Developed originally before wide
computer use
• Make fewer assumptions
• Need not involve population
parameters
• Results may be as exact as
parametric procedures © 1984-1994 T/Maker Co.
© 2011 Pearson Education, Inc
Disadvantages of
Nonparametric Tests
• May waste information
— If data permit using parametric
procedures
— Example: converting data from
ratio to ordinal scale
• Difficult to compute by hand for
large samples
• Tables not widely available
© 1984-1994 T/Maker Co.
Differences between independent groups
• Two samples –
compare mean value
for some variable of
interest
Parametric Nonparametric
t-test for
independent
samples
Wald-Wolfowitz
runs test
Mann-Whitney U
test
Kolmogorov-
Smirnov two
sample test
Differences between independent groups
• Multiple groups
Parametric Nonparametric
Analysis of
variance
(ANOVA/
MANOVA)
Kruskal-Wallis
analysis of ranks
Median test
Differences between dependent groups
• Compare two variables
measured in the same
sample
• If more than two variables
are measured in same
sample
Parametric Nonparametric
t-test for
dependent
samples
Sign test
Wilcoxon’s
matched pairs
test
Repeated
measures
ANOVA
Friedman’s two
way analysis of
variance
Cochran Q
Relationships between variables
• Two variables of
interest are
categorical
Parametric Nonparametric
Correlation
coefficient
Spearman R
Kendall Tau
Coefficient Gamma
Chi square
Phi coefficient
Fisher exact test
Kendall coefficient of
concordance

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Nonparametric-test_Fazlul.pptNonparametric-test_Fazlul.pptNonparametric-test_Fazlul.pptNonparametric-test_Fazlul.ppt

  • 1. Nonparametric Statistical Methods Dr. Md Fazlul Haque Associate Professor Dept. of Zoology University of Rajshahi
  • 2. Definition When the data is generated from process (model) that is known except for finite number of unknown parameters the model is called a parametric model. Otherwise, the model is called a non-parametric model The branch of inferential statistics devoted to distribution-free tests is called nonparametric. Distribution-free tests are statistical tests that do not rely on any underlying assumptions about the probability distribution of the sampled population.
  • 3. For example If you assume that your data has come from a normal distribution with mean  and standard deviation  (both unknown) then the data is generated from process (model) that is known except for two of parameters ( and  ). The model is called a parametric model. Models that do not assume normality (or some other distribution with a finite no. of parameters) are non-parametric
  • 4. © 2011 Pearson Education, Inc
  • 5. Scales of Measurement • Nominal scale:A nominal scale is really a list of categories to which objects can be classified. • Ordinal scale:is a measurement scale that assigns values to objects based on their ranking with respect to one another. • Interval scale: one unit on the scale represents the same magnitude on the trait or characteristic being measured across the whole range of the scale. For example, if anxiety were measured on an interval scale, then a difference between a score of 10 and a score of 11 would represent the same difference in anxiety as would a difference between a score of 50 and a score of 51. • Ratio scale: Ratio scales are like interval scales except they have true zero points.
  • 6. © 2011 Pearson Education, Inc Parametric Test Procedures • Involve population parameters — Example: population mean, standard deviations or probabilities • Require interval scale or ratio scale — Whole numbers or fractions — Example: height in inches (72, 60.5, 54.7) • Have stringent assumptions — Example: normal distribution • Examples: z-test, t-test, F-test
  • 7. © 2011 Pearson Education, Inc Nonparametric Test Procedures • Do not involve population parameters — Example: probability distributions, independence • Data measured on any scale — Ratio or interval — Ordinal  Example: good-better-best — Nominal  Example: male-female • Example: Wilcoxon rank sum test
  • 8. © 2011 Pearson Education, Inc Advantages of Nonparametric Tests • Used with all scales • Easier to compute — Developed originally before wide computer use • Make fewer assumptions • Need not involve population parameters • Results may be as exact as parametric procedures © 1984-1994 T/Maker Co.
  • 9. © 2011 Pearson Education, Inc Disadvantages of Nonparametric Tests • May waste information — If data permit using parametric procedures — Example: converting data from ratio to ordinal scale • Difficult to compute by hand for large samples • Tables not widely available © 1984-1994 T/Maker Co.
  • 10. Differences between independent groups • Two samples – compare mean value for some variable of interest Parametric Nonparametric t-test for independent samples Wald-Wolfowitz runs test Mann-Whitney U test Kolmogorov- Smirnov two sample test
  • 11. Differences between independent groups • Multiple groups Parametric Nonparametric Analysis of variance (ANOVA/ MANOVA) Kruskal-Wallis analysis of ranks Median test
  • 12. Differences between dependent groups • Compare two variables measured in the same sample • If more than two variables are measured in same sample Parametric Nonparametric t-test for dependent samples Sign test Wilcoxon’s matched pairs test Repeated measures ANOVA Friedman’s two way analysis of variance Cochran Q
  • 13. Relationships between variables • Two variables of interest are categorical Parametric Nonparametric Correlation coefficient Spearman R Kendall Tau Coefficient Gamma Chi square Phi coefficient Fisher exact test Kendall coefficient of concordance