Assessing Normality and
Data Transformations
Many statistical methods require that the numeric
variables we are working with have an approximate
normal distribution. For example, t-tests,
F-tests, and regression analyses all require in
some sense that the numeric variables are
approximately normally distributed.
1
Assessing Normality and
Data Transformations
Tools for Assessing Normality include
Histogram and Boxplot
Normal Quantile Plot (also called Normal
Probability Plot)
Goodness of Fit Tests such as
Anderson-Darling Test
Kolmogorov-Smirnov Test
Lillefor’s Test
Shapiro-Wilk Test
Problem: they don’t always agree!
2
Assessing Normality and
Data Transformations
The following examples are intended to illustrate
what normal data really looks like.
In each case you could produce conventional
descriptive statistics, a histogram with a
superimposed normal curve, and a normal scores
plot also called a normal probability plot.
3
Assessing Normality and
Data Transformations
Analyze > Descriptive Statistics > Explore
Under plots select histogram, descriptive
statistics and boxplots are default options plus a
Kolmogorov-Smirnov statistic
Analyze > Descriptive Statistics > P-P plot
Produces a probability plot Graphs > Legacy
Dialogs > Histogram
Tick – display normal curve
Graphs > Legacy Dialogs > Line
Select Simple and Groups of Cases the use
Define to choose the variable and select “cum %”
If you wish to superimpose a normal curve,
it is probably simpler in Excel!
4
Assessing Normality and
Data Transformations
If the data is “normal” the non-linear vertical axis
in a probability plot should result in an
approximately linear scatter plot representing the
raw data.
5
Assessing Normality and
Data Transformations
The ideal plot is exhibited by the Chest Sizes Of
Militiamen (see printed notes for fuller details).
Here is an example where the data is perfectly
normal. The P-P plot is a normal probability plot
with the data on the horizontal axis and the
expected z-scores if our data was normal on the
vertical axis. When our data is approximately
normal the spacing of the two will agree resulting
in a plot with observations lying on the reference
line in the normal probability plot.
6
Assessing Normality and
Data Transformations
Histogram of Chest
Empirical CDF of Chest
Normal
Normal
1200
Mean
StDev
N
1000
39.83
2.050
5738
Mean
StDev
N
100
39.83
2.050
5738
80
Percent
600
60
40
400
20
200
0
0
34
36
38
40
42
Chest
44
46
48
35
40
45
50
Chest
Boxplot of Chest
Probability Plot of Chest
Normal - 95% CI
Mean
StDev
N
AD
P-Value
99.99
99
39.83
2.050
5738
55.693
<0.005
95
Percent
Frequency
800
80
50
20
5
1
0.01
35
40
45
Chest
50
30
35
40
Chest
45
50
7
Assessing Normality and
Data Transformations
When the data is plotted vs. the expected zscores the normal probability plot shows right
skewness by a downward bending curve, see
Distribution Patterns (QRAT, see printed notes
for more details).
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Assessing Normality and
Data Transformations
Histogram of QRAT
Empirical CDF of QRAT
Normal
Normal
Mean
StDev
N
25
158.6
97.75
50
Percent
Frequency
158.6
97.75
50
80
20
15
10
60
40
20
5
0
Mean
StDev
N
100
0
0
120
240
QRAT
360
480
-100
0
100
Boxplot of QRAT
200
300
QRAT
400
500
600
Probability Plot of QRAT
Normal - 95% CI
99
Mean
StDev
N
AD
P-Value
95
90
158.6
97.75
50
4.552
<0.005
Percent
80
70
60
50
40
30
20
10
5
0
100
200
300
QRAT
400
500
600
1
-200
-100
0
100
200
QRAT
300
400
500
600
9
Assessing Normality and
Data Transformations
When the data is plotted vs. the expected zscores the normal probability plot shows left
skewness by an upward bending curve, see
Estimating the Speed of Light, see printed notes
for more details).
10
Assessing Normality and
Data Transformations
Histogram of Speed
Empirical CDF of Speed
Normal
Normal
Mean
StDev
N
40
26.21
10.75
66
Mean
StDev
N
100
26.21
10.75
66
80
Percent
Frequency
30
20
40
20
10
0
60
0
-40
-20
0
Speed
20
40
-50
-25
Boxplot of Speed
0
Speed
25
50
Probability Plot of Speed
Normal - 95% CI
99.9
Mean
StDev
N
AD
P-Value
99
Percent
95
90
26.21
10.75
66
5.884
<0.005
80
70
60
50
40
30
20
10
5
1
-50
-40
-30
-20
-10
0
Speed
10
20
30
40
0.1
-50
-25
0
25
Speed
50
75
11
Transformations to Improve
Normality (removing skewness)
Many statistical methods require that the numeric
variables you are working with have an
approximately normal distribution. Reality is that
this is often times not the case. One of the most
common departures from normality is skewness, in
particular, right skewness.
12
Tukey’s Ladder of Powers
Tukey (1977) describes an orderly way of reexpressing variables using a power transformation.
If a transformation for x of the type xλ, results
in an effectively linear probability plot, then we
should consider changing our measurement scale
for the rest of the statistical analysis. There is no
constraint on values of λ that we may consider.
Obviously choosing λ = 1 leaves the data
unchanged. Negative values of λ are also
reasonable. Tukey (1977) suggests that it is
convenient to simply define the transformation
when λ = 0 to be the logarithmic function rather
than the constant 1.
13
Tukey’s Ladder of Powers
A typical ladder is
Sometimes
and
are also included.
We go up the ladder (positive λ) to remove left
skewness and down the ladder (negative λ) to
remove right skewness.
Tukey, J. W. (1977) Exploratory Data Analysis.
Addison-Wesley, Reading, MA.
14
Tukey’s Ladder of Powers
15
Tukey’s Ladder of Powers
16
Tukey’s Ladder of Powers
In general if the normal distribution fits the data, then the
plotted points will roughly form a straight line. In addition the
plotted points will fall close to the fitted line. Also the AndersonDarling statistic will be small, and the associated p-value will be
larger than the chosen α-level (usually 0.05). So the test rejects
the hypothesis of normality when the p-value is less than or equal
to α.
17
Tukey’s Ladder of Powers
In general if the normal distribution fits the data, then the
plotted points will roughly form a straight line. In addition the
plotted points will fall close to the fitted line. Also the AndersonDarling statistic will be small, and the associated p-value will be
larger than the chosen α-level (usually 0.05). So the test rejects
the hypothesis of normality when the p-value is less than or equal
to α.
18
Tukey’s Ladder of Powers
To test for normality is SPSS you can perform a Kolmogorov-Smirnov Test,
Analyze > Nonparametric tests > Legacty Dialogs > 1-Sample KolmogorovSmirnov Test
The Asymp. Sig. (2 tailed) value is also known as the p-value. This tells you
the probability of getting the results if the null hypothesis were actually
true (i.e. it is the probability you would be in error if you rejected the null19
hypothesis).
Tukey’s Ladder of Powers
To test for normality is SPSS you can perform a Kolmogorov-Smirnov Test,
Analyze > Nonparametric tests > 1-Sample Kolmogorov-Smirnov Test
The Asymp. Sig. (2 tailed) value is also known as the p-value. This tells you
the probability of getting the results if the null hypothesis were actually
true (i.e. it is the probability you would be in error if you rejected the null
hypothesis).
20
Tukey’s Ladder of Powers
The hypothesis are
H0
H1
the distribution of x is normal
the distribution of x is not normal
If the p-value is less than .05, you reject the normality
assumption, and if the p-value is greater than .05, there
is insufficient evidence to suggest the distribution is not
normal (meaning that you can proceed with the
assumption of normality.)
In summary if the test is significant (lower than or equal
21
to 0.05) implies the data is not normally distributed.
Does It Really Matter?
“Students t test and more generally the ANOVA F test
are robust to non-normality” (Fayers 2011).
However
“Thus a clearer statement is that t tests and ANOVA are
‘robust against type-I errors’. This of course accords
with the enthusiasm that many researchers have in
obtaining ‘‘significant’’ p values.
The aim of this article is to show that type-II errors can
be substantially increased if non-normality is ignored.”
(Fayers 2011).
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Does It Really Matter?
Alphas, betas and skewy distributions: two ways of
getting the wrong answer Paper
Peter Fayers
Adv. Health Sci. Educ. Theory Pract. 2011 16(3) 291–296.
Wilcox, R. R. (2005). Introduction to Robust Estimation
and Hypothesis Testing (2nd ed.). Burlington MA:
Elsevier Academic Press. ISBN 978-0-12-751542-7.
Robustness to Non-Normality of Common Tests for the
Many-Sample Location Problem Paper
Khan A. and Rayner G.D.
Journal Of Applied Mathematics And Decision Sciences,
23
2003, 7(4), 187:206