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More Non-normal Distributions Fig. 2-7, p. 26
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Parametric vs. Parametric vs. Nonparametric Nonparametric
Statistical InferenceStatistical Inference
BIOL457BIOL45727 January 201627 January 2016
Normal vs. Non-normalNormal vs. Non-normalFig. 4-6, p. 89Fig. 4-6, p. 89
More Non-normal DistributionsMore Non-normal DistributionsFig. 2-7, p. 26Fig. 2-7, p. 26
Converting Data to the Converting Data to the Standard Normal (“Bell”) CurveStandard Normal (“Bell”) Curve
Curve with mean of 0 and SD of 1Curve with mean of 0 and SD of 1 Convert sample mean to 0 via subtraction of Convert sample mean to 0 via subtraction of
mean from each data pointmean from each data point Divide transformed data points by SDDivide transformed data points by SD
Fig. 2-9, p. 30Fig. 2-9, p. 30
Fig. 4-3, p. 83Fig. 4-3, p. 83
Characteristics of the Normal DistributionCharacteristics of the Normal DistributionFig. 4-4, p. 85Fig. 4-4, p. 85
SymmetricSymmetric 68.26% of data within 1 68.26% of data within 1
SD of meanSD of mean Curve inflection points Curve inflection points
are at are at ± 1 SD± 1 SD 95.44% within 2 SDs95.44% within 2 SDs 99.74% within 3 SDs99.74% within 3 SDs
Parametric or Non-parametric Test?Parametric or Non-parametric Test?
Normality is often assumed without examination of Normality is often assumed without examination of data distributiondata distribution
Under some circumstances, non-normality can be Under some circumstances, non-normality can be assumedassumed ExEx:: Texas map turtle Texas map turtle
habitat data (depth, habitat data (depth, distance from shore)distance from shore)
for 60 turtles but just for 60 turtles but just 18 trapping locations18 trapping locations
Distribution DiagnosticsDistribution Diagnostics Why be concerned?Why be concerned? Many statistical tests assume data come from a Many statistical tests assume data come from a
parametric distributionparametric distribution Many parametric tests have nonparametric Many parametric tests have nonparametric
equivalents (but with less statistical power)equivalents (but with less statistical power) ExsExs: : Mann-Whitney Mann-Whitney UU rather than rather than tt
Kruskal-Wallis rather than ANOVAKruskal-Wallis rather than ANOVASpearman’s rho rather than Pearson’s Spearman’s rho rather than Pearson’s
rr
Distribution DiagnosticsDistribution Diagnostics 1) Kurtosis1) Kurtosis
Leptokurtic—tails smaller than those of a normal Leptokurtic—tails smaller than those of a normal distributiondistribution
Mesokurtic—normal bell curveMesokurtic—normal bell curve Platykurtic—tails larger than those of a normal Platykurtic—tails larger than those of a normal
distributiondistribution
Leptokurtic, Mesokurtic, Leptokurtic, Mesokurtic, and Platykurtic Distributionsand Platykurtic Distributions
Distribution DiagnosticsDistribution Diagnostics 2) Skew2) Skew
Positive skew—modal hump toward left (lower Positive skew—modal hump toward left (lower values)values)
Symmetric—modal hump in middle (normal)Symmetric—modal hump in middle (normal) Negative skew—modal hump toward right (higher Negative skew—modal hump toward right (higher
values)values)
Positive and Negative SkewPositive and Negative Skew
The Kolmogorov-Smirnov TestThe Kolmogorov-Smirnov Test Compares sample distribution to any of a Compares sample distribution to any of a
variety of theoretical distributionsvariety of theoretical distributions Most commonly, the normal distributionMost commonly, the normal distribution
pp-value evaluates the probability data are -value evaluates the probability data are drawn from the specified distributiondrawn from the specified distribution
Transforming Non-Normal Data Transforming Non-Normal Data to Achieve Normalityto Achieve Normality
Allows use of more powerful parametric tests Allows use of more powerful parametric tests rather than nonparametric testsrather than nonparametric tests
Each data point (Each data point (xx) transformed, as…) transformed, as… ……ln(ln(xx)) ……xx22
…√…√xx ……log(log(xx+1)+1) ……eexx
__
Transformation to Achieve NormalityTransformation to Achieve NormalityFig. 6-1, p. 140Fig. 6-1, p. 140
Transformation to Achieve NormalityTransformation to Achieve NormalityFig. 6-2, p. 142Fig. 6-2, p. 142