statistical tests for computational intelligence research and human subjective tests

Statistical Tests for Computational Intelligence Research and

Human Subjective Tests

Hideyuki TAKAGIKyushu University, Japan

http://www.design.kyushu-u.ac.jp/~takagi/ver. March 26, 2015ver. July 15, 2013ver. July 11, 2013ver. April 23, 2013

Slides are downloadable fromhttp://www.design.kyushu-u.ac.jp/~takagi

ContentsContents2 groups n groups (n > 2)

datadistribution

・ unpaired ｔ -test

・ sign test

・ Wilcoxon signed-ranks test ・ Friedman test

・ Kruskal-Wallis test

・ one-way ANOVA

・ two-way ANOVA

(no

norm

ality

)

one-waydata

two-waydata

Par

am

etric

Tes

tN

on-p

ara

met

ric T

est

(nor

mal

ity)

unp

aire

d(in

dep

ende

nt)

pair

ed(r

ela

ted

)un

pai

red

(ind

epen

dent

)pa

ired

(rel

ate

d)

・ paired ｔ -test

・ Mann-Whitney U-test

AN

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)

＋ Scheffé's method of paired comparison for Human Subjective Tests

fitne

ss

generations

fitne

ss

generations

conventionalEC

proposed EC1 proposed EC2

How to Show Significance?How to Show Significance?

conventionalEC

Just compare averages visually?It is not scientific.

Fig. XX Average convergence curves of n times of trial runs.

How to Show Significance?How to Show Significance?

sound made byconventional IEC

sound made byproposed IEC1

sound made byproposed IEC2

Sound design concept: exiting

Which method is good to make exiting sound?How to show it?

You cannot show the superiority of your method without statistical tests.You cannot show the superiority of your method without statistical tests.

Papers without statistics tests may be rejected.

statistical test

My method is significantly better!

Which Test Should We Use?Which Test Should We Use?2 groups n groups (n > 2)

datadistribution


・ sign test



・ one-way ANOVA

・ two-way ANOVA

(no

norm

ality

)

one-waydata

two-waydata

Pa

ram

etr

ic T

est

No

n-p

ara

me

tric

Tes

t(n

orm

ality

)

unpa

ired

(ind

epen

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)pa

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(rel

ated

)un

paire

d(in

dep

ende

nt)

paire

d(r

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ed)



AN

OV

A(A

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of V

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Which Test Should We Use?Which Test Should We Use?2 groups n groups (n > 2)

datadistribution


・ sign test



・ one-way ANOVA

・ two-way ANOVA

(no

norm

ality

)

one-waydata

two-waydata

Pa

ram

etr

ic T

est

No

n-p

ara

me

tric

Tes

t(n

orm

ality

)

unpa

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(ind

epen

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(rel

ated

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d(in

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nt)

paire

d(r

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ed)



AN

OV

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of V

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nce)

n-th generation n-th generation

2 groups n groups (n > 2)

datadistribution


・ sign test



・ one-way ANOVA

・ two-way ANOVA

one-waydata

two-waydata

unpa

ired

(ind

epen

dent

)pa

ired

(rel

ated

)un

paire

d(in

dep

ende

nt)

paire

d(r

elat

ed)



AN

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of V

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(no

norm

ality

)P

ara

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-pa

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ic T

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(nor

mal

ity)

Which Test Should we Use?Which Test Should we Use?

• Anderson-Darling test• D'Agostino-Pearson test• Kolmogorov-Smirnov test• Shapiro-Wilk test• Jarque–Bera test ・・・・

Normality Test

Find a free Excel add-in or software.


datadistribution


・ sign test



・ one-way ANOVA

・ two-way ANOVA

one-waydata

two-waydata



AN

OV

A(A

naly

sis

of V

aria

nce)

(no

norm

ality

)P

ara

me

tric

Te

stN

on

-pa

ram

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ic T

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(nor

mal

ity)

Which Test Should We Use?Which Test Should We Use?

unpa

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)pa

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(rel

ated

)un

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d(in

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initialdata

#

conventional

proposed

1 4.23 2.51

2 3.21 3.30

3 3.63 3.75

4 4.42 3.22

5 4.08 3.99

6 3.98 3.65

unpaired data(independent)

paired data(related)

group A group B

4.23 2.51

3.21 3.3

3.63 3.75

4.42 3.22

4.08 3.99

3.98 3.65


datadistribution


・ sign test



・ one-way ANOVA

・ two-way ANOVA

one-waydata

two-waydata



AN

OV

A(A

naly

sis

of V

aria

nce)

(no

norm

ality

)P

ara

me

tric

Te

stN

on

-pa

ram

etr

ic T

est

(nor

mal

ity)


unpa

ired

(ind

epen

dent

)pa

ired

(rel

ated

)un

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d(in

dep

ende

nt)

paire

d(r

elat

ed)



A group data B group data initialdata

#GA proposed


datadistribution


・ sign test



・ one-way ANOVA

・ two-way ANOVA

one-waydata

two-waydata



AN

OV

A(A

naly

sis

of V

aria

nce)

(no

norm

ality

)P

ara

me

tric

Te

stN

on

-pa

ram

etr

ic T

est

(nor

mal

ity)


unpa

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(ind

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)pa

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(rel

ated

)un

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d(in

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d(r

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A group data B group data initialdata

#GA proposed

Q1: Which tests are more sensitive, those for unpaired data or paired data?

A1: Statistical tests for paired data because of more data information.


datadistribution


・ sign test



・ one-way ANOVA

・ two-way ANOVA

one-waydata

two-waydata



AN

OV

A(A

naly

sis

of V

aria

nce)

(no

norm

ality

)P

ara

me

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Te

stN

on

-pa

ram

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(nor

mal

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unpa

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(ind

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(rel

ated

)un

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d(in

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nt)

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d(r

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Q2: How should you design your experimental 　　　 conditions to use statistical tests for 　　　 paired data and reduce the # of trial runs?A2: Use the same initialized data for the set of (method A, method B) at each trial run. 　

n-th generation

significant?


datadistribution


・ sign test



・ one-way ANOVA

・ two-way ANOVA

one-waydata

two-waydata

unpa

ired

(ind

epen

dent

)pa

ired

(rel

ated

)un

paire

d(in

dep

ende

nt)

paire

d(r

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AN

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(no

norm

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ara

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-pa

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(nor

mal

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Which Test Should we Use?Which Test Should we Use?Q3: Which statistical tests are sensitive, parametric tests or non-parametric ones and why?

A3: Parametric tests which can use information of assumed data distribution. 　

t -Testt -Test2 groups n groups (n > 2)

datadistribution


・ sign test



・ one-way ANOVA

・ two-way ANOVA

(no

norm

ality

)

one-waydata

two-waydata

Pa

ram

etr

ic T

est

No

n-p

ara

me

tric

Tes

t(n

orm

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)

unpa

ired

(ind

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)pa

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ｔ -test

t-TestHow to Show Significance?

t-TestHow to Show Significance?

n-th generation

significant?

A B

12 10

14 9

14 7

11 15

16 11

19 10

significantdifference?

Conditions to use t-tests:(1) normality(2) equal variances (not essential though )

Test this difference with assuming no difference.（ null hypothesis ）

t-Testt-Test

Conditions to use t-tests:(1) normality(2) equal variances (not essential though )

A B

12 10

14 9

14 7

11 15

16 11

19 10

significantdifference?

Test this difference with assuming no difference.（ null hypothesis ）

Normality Test• Anderson-Darling test• D'Agostino-Pearson test• Kolmogorov-Smirnov test• Shapiro-Wilk test• Jarque–Bera test ・・・・

F-Test

When (p > 0.05), we assume that there is no significant difference between σ2

A and σ2B .

t-Testt-Test

t-Testt-TestExcel (32 bits version only?) has t-tests and ANOVA in Data Analysis Tools. You must install its add-in. (File -> option -> add-in, and set its add-in.)

t-Testt-Test

(1) t-Test: Pairs two sample for means

(2) t-Test: Two-sample assuming equal variances

(3) t-Test: Two-sample assuming unequal variances: Welch's t-test

n-th generation

significant? This is a case when each pair of two methods with the same initial condition.

t-Testt-Test

A B 4.23 2.51 3.21 3.31 3.63 3.75 4.42 3.22 4.08 3.99 3.98 3.65 3.68 3.35 4.18 3.93 3.85 3.91 3.71 3.82

　 Variable 1 Variable 2

Mean 3.897 3.544

Variance 0.125823333 0.208693333

Observations 10 10

Pearson Correlation -0.161190073Hypothesized Mean Difference

0

df 9

t Stat 1.794964241

P(T<=t) one-tail 0.053116886

t Critical one-tail 1.833112933

P(T<=t) two-tail 0.106233772

t Critical two-tail 2.262157163　

sample data t-Test: Paired Two Sample for Means

t-Testt-Test

A B 4.23 2.51 3.21 3.31 3.63 3.75 4.42 3.22 4.08 3.99 3.98 3.65 3.68 3.35 4.18 3.93 3.85 3.91 3.71 3.82

　 Variable 1 Variable 2

Mean 3.897 3.544

Variance 0.125823333 0.208693333

Observations 10 10

Pearson Correlation -0.161190073Hypothesized Mean Difference

0

df 9

t Stat 1.794964241

P(T<=t) one-tail 0.053116886

t Critical one-tail 1.833112933

P(T<=t) two-tail 0.106233772

t Critical two-tail 2.262157163　

sample data t-Test: Paired Two Sample for Means

2.5% 2.5% 5%

When p-value is less than 0.01 or 0.05, we assume that there is significant difference with the level of significance of (p < 0.01) or (p < 0.05).

A > B A < BA ≈ B When A>B never happens, you may use a one-tail test.

t-Testt-Test

(1) t-Test: Pairs two sample for means (2) t-Test: Two-sample assuming equal variances

Difference between two groups is significant (p < 0.01).

We cannot say that there is a significant difference between two group.

ANOVA: Analysis of VarianceANOVA: Analysis of Variance2 groups n groups (n > 2)

datadistribution


・ sign test



・ one-way ANOVA

・ two-way ANOVA

(no

norm

ality

)

one-waydata

two-waydata

Pa

ram

etr

ic T

est

No

n-p

ara

me

tric

Tes

t(n

orm

ality

)

unpa

ired

(ind

epen

dent

)pa

ired

(rel

ated

)un

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d(in

dep

ende

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d(r

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AN

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ANOVA

ANOVA: Analysis of VarianceANOVA: Analysis of Variance

n-th generation

sign

ifican

t?

If data have

(1) normality

(2) equal variances

A B C

11.0 12.8 9.4

9.3 11.3 12.4

11.5 9.5 16.8

16.4 14.0 14.3

16.0 15.2 17.0

15.0 13.0 14.6

12.8 12.4 17.0

13.6 15.0 14.3

13.0 12.4 15.6

12.0 17.8 15.0

13.4 12.6 18.6

10.0 13.4 12.4

10.8 16.8 15.4

1. Analysis of more than two data groups.2. Normality and equal variance are required.

C A B

Excel has ANOVA in Data Analysis Tools.


A B C

11.0 12.8 9.4

9.3 11.3 12.4

11.5 9.5 16.8

16.4 14.0 14.3

16.0 15.2 17.0

15.0 13.0 14.6

12.8 12.4 17.0

13.6 15.0 14.3

13.0 12.4 15.6

12.0 17.8 15.0

13.4 12.6 18.6

10.0 13.4 12.4

10.8 16.8 15.4

1. Analysis of more than two data groups.2. Normality and equal variance are required.

C A B

three t-tests one ANOVA=1-(1-0.05)3 = 0.14

Three times of t-test with (p<0.05) equivalent one ANOVA (p<0.14).

Excel has ANOVA in Data Analysis Tools.


Check it using the Bartlett test.


n-th generation

When data are independent, use one-way ANOVA (single factor ANOVA).

When data correspond each other, use two-way ANOVA (two-factor ANOVA).


When data are independent, use one-way ANOVA (single factor ANOVA).

When data correspond each other, use two-way ANOVA (two-factor ANOVA).

Q1: What are "single factor" and "two factors"?

A1: A column factor (e.g. three groups) and a sample factor (e.g. initialized condition).

column factor

sam

ple

fact

or

column factor


column factor

sam

ple

fact

or

column factor

one-factor (one-way) ANOVA two-factor (two-way) ANOVA

group A group B group C4.23 2.51 3.043.21 3.3 2.893.63 3.75 3.554.42 3.22 4.394.08 3.99 3.863.98 3.65 3.53.75 2.62 3.63.22 2.93 3.21

initial condition

group A group B group C

#1 4.23 2.51 3.04#2 3.21 3.3 2.89#3 3.63 3.75 3.55#4 4.42 3.22 4.39#5 4.08 3.99 3.86#6 3.98 3.65 3.5#7 3.75 2.62 3.6#8 3.22 2.93 3.21

We cannot say that three groups are significantly different. (p=0.089)

There are significant difference somewhere among three groups. (p<0.05)


Output of the one-way ANOVASource of Variation SS df MS F P-value F crit

Between Groups 6.11342 2 3.05671 15.30677 3.6E-05 3.354131Within Groups 5.39181 27 0.199697

Total 11.50523 29　　　　

Output of the two-way ANOVASource of Variation SS df MS F P-value F crit

Sample 0.755233 2 0.377617 2.755097 0.103596 3.885294Columns 3.582272 1 3.582272 26.13631 0.000256 4.747225Interaction 0.139411 2 0.069706 0.508573 0.613752 3.885294Within 1.644733 12 0.137061

Total 6.12165 17　　　　

When (p-value < 0.01 or 0.05), there is(are) significant differencesomewhere among data groups.

• Significant difference among Sample (e.g. initial conditions) cannot be found (p > 0.05).

• Significant difference can be found somewhere among Columns (e.g. three methods) (p < 0.01).

• We need not care an interaction effect between two factors (e.g. initial condition vs. methods) (p > 0.05).

A B C

11.0 12.8 9.4

9.3 11.3 12.4

11.5 9.5 16.8

16.4 14.0 14.3

16.0 15.2 17.0

15.0 13.0 14.6

12.8 12.4 17.0

13.6 15.0 14.3

13.0 12.4 15.6

12.0 17.8 15.0

13.4 12.6 18.6

10.0 13.4 12.4

10.8 16.8 15.4

Sam

ple

fact

or

Columnfactor


Source of Variation SS df MS F P-value F critSample 0.755233 2 0.377617 2.755097 0.103596 3.885294Columns 3.582272 1 3.582272 26.13631 0.000256 4.747225Interaction 0.139411 2 0.069706 0.508573 0.613752 3.885294Within 1.644733 12 0.137061

Total 6.12165 17　　　　

A B C

11.0 12.8 9.4

9.3 11.3 12.4

11.5 9.5 16.8

16.4 14.0 14.3

16.0 15.2 17.0

15.0 13.0 14.6

12.8 12.4 17.0

13.6 15.0 14.3

13.0 12.4 15.6

12.0 17.8 15.0

13.4 12.6 18.6

10.0 13.4 12.4

10.8 16.8 15.4

Sam

ple

fact

or

Columnfactor

Q1: Where is significant among A, B, and C?

A1: Apply multiple comparisons between all pairs among columns.(Fisher's PLSD method, Scheffé method, Bonferroni-Dunn test, Dunnett method, Williams method, Tukey method, Nemenyi test, Tukey-Kramer method, Games/Howell method, Duncan's new multiple range test, Student-Newman-Keuls method, etc. Each has different characteristics.)

significant?

Non-Parametric TestsNon-Parametric Tests


datadistribution


・ sign test



・ one-way ANOVA

・ two-way ANOVA

(no

norm

ality

)

one-waydata

two-waydata

Pa

ram

etr

ic T

est

No

n-p

ara

me

tric

Tes

t(n

orm

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)

unpa

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(ind

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)pa

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(rel

ated

)un

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d(in

dep

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paire

d(r

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AN

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of V

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If normality and equal variances are not guaranteed, use non-parametric tests.

Mann-Whitney U-testMann-Whitney U-test2 groups n groups (n > 2)

datadistribution


・ sign test



・ one-way ANOVA

・ two-way ANOVA

(no

norm

ality

)

one-waydata

two-waydata

Pa

ram

etr

ic T

est

No

n-p

ara

me

tric

Tes

t(n

orm

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unpa

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)pa

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(rel

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Mann-Whitney U-test(Wilcoxon-Mann-Whitney test, two sample Wilcoxon test)


n-th generation

?

?no normality

1. Comparison of two groups.2. Data have no normality.3. There are no data corresponding

between two groups (independent).



1. Calculate a U value.

0

2

34

U = 0 + 2 + 3 + 4 = 9U' = 11 (U + U' = n1n2)

when two values are the same, count as 0.5.( )

2. See a U-test table.

Mann-Whitney U-test (cont.)(Wilcoxon-Mann-Whitney test, two sample Wilcoxon test)

Mann-Whitney U-test (cont.)(Wilcoxon-Mann-Whitney test, two sample Wilcoxon test)

• Use the smaller value of U or U'.• When n1 ≤ 20 and n2 ≤ 20 , see a Mann-Whitney test table.

(where n1 and n2 are the # of data of two groups.)• Otherwise, since U follows the below normal distribution roughly,

normalize U as and check a standard normal distribution table

with the , where and .

12

)1(,

2, 2121212 nnnnnn

NN UU

U

UUz

221nn

U 12

)1( 2121

nnnnUz

Use an Excel function to calculate the p-value for the z-value: p-value = 1 - NORM.S.DIST( z )

Examples: Mann-Whitney U-test(Wilcoxon-Mann-Whitney test, two sample Wilcoxon test)

Examples: Mann-Whitney U-test(Wilcoxon-Mann-Whitney test, two sample Wilcoxon test)

U = 9U' = 11

0

2

34

00.5

2.54

5

3.5

55

5

U = 23.5U' = 1.5

U = 12U' = 13

Ex.1 Ex.2 Ex.3

4 5 6・・・

・・・

ー・・・

・・・

・・・

4 0 1 2・・・

5 2 3・・・

・・・

・・・

・・・

n1

n2

(p < 0.05)

4 5 6・・・

・・・

ー・・・

・・・

・・・

4 ーー 0・・・

5 1 1・・・

・・・

・・・

・・・

n1

n2

(p < 0.01)

5

(p > 0.05)

(p > 0.05)

(p >

0.0

5)

significant (p < 0.05)

Exercise: Mann-Whitney U-test(Wilcoxon-Mann-Whitney test, two sample Wilcoxon test)

Exercise: Mann-Whitney U-test(Wilcoxon-Mann-Whitney test, two sample Wilcoxon test)

2.5

45

6

66

U = 29.5U' = 6.5

4 5 6 7

3 ー 0 1 1

4 0 1 2 3

5 2 3 5

6 5 6

n1

n2

(p < 0.05)

4 5 6 7

3 ーーーー

4 ーー 0 0

5 1 1 1

6 2 3

n1

n2

(p < 0.01)

Since U' > 5, (p > 0.05): significance is not found( )

Sign TestSign Test2 groups n groups (n > 2)

datadistribution


・ sign test



・ one-way ANOVA

・ two-way ANOVA

(no

norm

ality

)

one-waydata

two-waydata

Pa

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No

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unpa

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（ 1 ） Sign Test

（ 2 ） Wilcoxon's Signed Ranks Test

significance test between the # of winnings and losses

173 174

143 137

158 151

156 143

176 180

165 162

- ++ -+ -+ -- ++ -

-1+6+7

+13-4+3

data of 2 groups

# of winnings and losses

the level of winnings/losses

Sign TestSign Test

significance test using both the # of winnings and losses and the level of winnings/losses

n-th generation

Sign TestSign Test

1. Calculate the # of winnings and losses by comparing runs with the same initial data.

2. Check a sign test table to show significance of two methods.

Sign TestSign TestFig.3 in Y. Pei and H. Takagi, "Fourier analysis of the fitness landscape for evolutionary search acceleration," IEEE Congress on Evolutionary Computation (CEC), pp.1-7, Brisbane, Australia (June 10-15, 2012).

The (+,-) marks show whether our proposed methods converge significantly better or poorer than normal DE, respectively, (p ≤0.05).

Fig.2 in the same paper.

Task ExampleWhether performances of pattern recognition methods A and B are significantly different?

n1 cases: Both methods succeeded.n2 cases: Method A succeeded, and method B failed.n3 cases: Method A failed, and method B succeeded.n4 cases: Both methods failed.

1. Set N = n2 + n3.2. Check the right table with the N.3. If min(n2, n3) is smaller than the number for the N,

we can say that there is significant difference with the significant risk level of XX.

How to check?

Whether there is significant difference for n2 = 12 and n3 = 28?

Exercise

level of significance

Sign TestSign Test %%

ANSWER:Check the right table with N = 40.As n2 is bigger than 11 and smaller than 13, we can say that there is a significant difference between two with (p < 0.05) but cannot say so with (p < 0.01).

% %



%%Sign TestSign Test

Let's think about the case of N = 17.

To say that n1 and n2 are significantly different,

(n1 vs. n2) = (17 vs. 0), (16 vs. 1), or (15 vs. 2) (p < 0.01)

or(n1 vs. n2) = (14 vs. 3) or (13 vs. 4) (p < 0.05)

Check the significance of:

Exercise: Sign TestExercise: Sign Test level of significance

%%

16 vs. 4

14 vs. 1

9 vs. 3

18 vs. 5

Wilcoxon Signed-Ranks TestWilcoxon Signed-Ranks Test2 groups n groups (n > 2)

datadistribution


・ sign test



・ one-way ANOVA

・ two-way ANOVA

(no

norm

ality

)

one-waydata

two-waydata

Pa

ram

etr

ic T

est

No

n-p

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n-th generation

Wilcoxon Signed-Ranks TestWilcoxon Signed-Ranks Test

Q: When a sign test could not show significance, how to do?A: Try the Wilcoxon signed-ranks test. It is more sensitive

than a simple sign test due to more information use.

（ 1 ） Sign Test

（ 2 ） Wilcoxon's Signed Ranks Test

significance test between the # of winnings and losses

173 174

143 137

158 151

156 143

176 180

165 162

- ++ -+ -+ -- ++ -

-1+6+7

+13-4+3

data of 2 groups

# of winnings and losses

the level of winnings/losses

significance test using both the # of winnings and losses and the level of winnings/losses



v (system A) v (system B) difference d rank of |d| add sign to the ranks

rank of fewer # of signs

182 163 19 7 7169 142 27 8 8172 173 － 1 1 － 1 1143 137 6 4 4158 151 7 5 5156 143 13 6 6176 172 4 3 3165 168 － 3 2 － 2 2

(step 1) (step 2) (step 3) (step 4)

(step 5) )4(# StepofT

3

8n

(step 6)

Wilcoxon test table

Example:

(step 6)

n = 8T = 3

Wilcoxon Test Table: significance point of T

T=3 ≤ 3 (n=8, p<0.05), then difference between systems A and B is significant.

T=3 > 0 (n=8, p<0.01), then we cannot say there is a significant difference.

one-tail p < 0.025 p < 0.005

two-tail p < 0.05 p < 0.01

n = 6789

10111213141516171819202122232425

02358

101317212529344046525865738189

013579

121519232732374248546168

When n > 25As T follows the below normal distribution roughly,

normalize T as the below and check a standard normal distribution table with the z; see andin the above equation.

24

)12)(1(,

4

)1(, 2 nnnnn

NN TT

T

TTz

T T




176 163 13 7 → 6.5 6.5142 142 0172 173 － 1 1 － 1 1143 137 6 4 4158 151 7 5 5156 143 13 6 → 6.5 6.5176 172 4 3 3165 168 － 3 2 － 2 2


Tips:1. When d = 0, ignore the data.2. When there are the same ranks of |d|,

give average ranks.

10

12

3

45

6

7 89

Give the average rank 6.5 = (5+6+7+8)/4.

Tip #1Tip #2

Tip #2

Exercise 1: Wilcoxon Signed-Ranks TestExercise 1: Wilcoxon Signed-Ranks Test(step 1) (step 2) (step 3) (step 4)



182 163

169 142

173 172

143 137

158 151

156 143

176 172

165 168

n = (step 5) )4(# StepofT

(step 6)

Wilcoxon test table

T =

n = 8

Exercise 1: Wilcoxon Signed-Ranks TestExercise 1: Wilcoxon Signed-Ranks Test(step 1) (step 2) (step 3) (step 4)


(step 6)

Wilcoxon test table



182 163 19 7 7

169 142 27 8 8

173 172 1 1 1

143 137 6 4 4

158 151 7 5 5

156 143 13 6 6

176 172 4 3 3

165 168 -3 2 -2 2

T = 2

As T(=2) < 3, there is a significant difference between A and B (p<0.05).But, as 0 < T(=2), we cannot say so with the significance level of (p<0.01).



(step 6)

Wilcoxon test table



27 31

20 25

34 33

25 27

31 31

23 29

26 27

24 30

35 34

n =

Exercise 2: Wilcoxon Signed-Ranks TestExercise 2: Wilcoxon Signed-Ranks Test

n = 8 (no count for d = 0.)



(step 6)

Wilcoxon test table



27 31 -4 5 -5

20 25 -5 6 -6

34 33 1 2 2 2

25 27 -2 4 -4

31 31 0

23 29 -6 7.5 -7.5

26 27 -1 2 -2

24 30 -6 7.5 -7.5

35 34 1 2 2 2

(No need to care the case of d = 0.)

T = 4

As T > 3, we cannot say that there is a significant difference between A and B.


n-th generation

Explain how to apply this test to test whether two groups are significantly different at the below generation?


Kruskal-Wallis TestKruskal-Wallis Test2 groups n groups (n > 2)

datadistribution


・ sign test



・ one-way ANOVA

・ two-way ANOVA

(no

norm

ality

)

two-waydata

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Kruskal-Wallis TestKruskal-Wallis Test

n-th generation

1. Comparison of more than two groups.2. Data have no normality.3. There are no data corresponding

among groups (independent). ??? no

nor

mal

ity

Kruskal-Wallis TestKruskal-Wallis Test

Let's use ranks of data.

1

23

45

6 7

8 910

11 121314

1516

17

Kruskal-Wallis TestKruskal-Wallis TestN: total # of datak: # of groupsni: # of data of group iRi : sum of ranks of group i

R1 = 38 R2 = 69 R3 = 46

1. Rank all data.2. Calculate N, k, ni and Ri .3. Calculate statistical value H.

4. If k = 3 and N ≤ 17, compare the H with a significant point in a Kruskal-Wallis test table.Otherwise, assume that H follows the χ2 distribution and test the H using a χ2 distribution table of (k-1) degrees of freedom

)1(3)1(

12

1

2

Nn

R

NNH

k

i i

i

1

23

4 56 7

8 91011 1213

141516

17

How to Test

Example: Kruskal-Wallis TestExample: Kruskal-Wallis Testn1 n2 n3 p < 0.05 p < 0.01 n1 n2 n3 p < 0.05 p < 0.012 2 2 - - 3 3 3 5.606 7.200 2 2 3 4.714 - 3 3 4 5.791 6.746 2 2 4 5.333 - 3 3 5 6.649 7.079 2 2 5 5.160 6.533 3 3 6 5.615 7.410 2 2 6 5.346 6.655 3 3 7 5.620 7.228 2 2 7 5.143 7.000 3 3 8 5.617 7.350 2 2 8 5.356 6.664 3 3 9 5.589 7.422 2 2 9 5.260 6.897 3 3 10 5.588 7.372 2 2 10 5.120 6.537 3 3 11 5.583 7.418 2 2 11 5.164 6.766 3 4 4 5.599 7.144 2 2 12 5.173 6.761 3 4 5 5.656 7.445 2 2 13 5.199 6.792 3 4 6 5.610 7.500 2 3 3 5.361 - 3 4 7 5.623 7.550 2 3 4 5.444 6.444 3 4 8 5.623 7.585 2 3 5 5.251 6.909 3 4 9 5.652 7.614 2 3 6 5.349 6.970 3 4 10 5.661 7.617 2 3 7 5.357 6.839 3 5 5 5.706 7.578 2 3 8 5.316 7.022 3 5 6 5.602 7.591 2 3 9 5.340 7.006 3 5 7 5.607 7.697 2 3 10 5.362 7.042 3 5 8 5.614 7.706 2 3 11 5.374 7.094 3 5 9 5.670 7.733 2 3 12 5.350 7.134 3 6 6 5.625 7.725 2 4 4 5.455 7.036 3 6 7 5.689 7.756 2 4 5 5.273 7.205 3 6 8 5.678 7.796 2 4 6 5.340 7.340 3 7 7 5.688 7.810 2 4 7 5.376 7.321 4 4 4 5.692 7.654 2 4 8 5.393 7.350 4 4 5 5.657 7.760 2 4 9 5.400 7.364 4 4 6 6.681 7.795 2 4 10 5.345 7.357 4 4 7 5.650 7.814 2 4 11 5.365 7.396 4 4 8 5.779 7.853 2 5 5 5.339 7.339 4 4 9 5.704 7.910 2 5 6 5.339 7.376 4 5 5 5.666 7.823 2 5 7 5.393 7.450 4 5 6 5.661 7.936 2 5 8 5.415 7.440 4 5 7 5.733 7.931 2 5 9 5.396 7.447 4 5 8 5.718 7.992 2 5 10 5.420 7.514 4 6 6 5.724 8.000 2 6 6 5.410 7.467 4 6 7 5.706 8.039 2 6 7 5.357 7.491 5 5 5 5.780 8.000 2 6 8 5.404 7.522 5 5 6 5.729 8.028 2 6 9 5.392 7.566 5 5 7 5.708 8.108 2 7 7 5.398 7.491 5 6 6 5.765 8.124 2 7 8 5.403 7.571

Kruskal-Wallis Test Table(for k = 3 and N ≤17)

N = n1+n2+n3 = 17 datak = 3 groups(n1, n2, n3) = (6, 5, 6)(R1, R2, R3) = (38, 69, 46)

)1(3)1(

12

1

2

Nn

R

NNH

k

i i

i

)117(36

46*46

5

69*69

6

38*38

)117(17

12

= 6.609

Since significant points of (p<0.05) and (p<0.01) for (n1, n2, n3) = (6, 5, 6) are 5.765 and 8.124, respectively, there are significant difference(s) somewhere among three groups (p<0.05).

6.6098.1245.765

significance point of (p<0.05)


Example: Kruskal-Wallis TestExample: Kruskal-Wallis Testn1 n2 n3 p < 0.05 p < 0.01 n1 n2 n3 p < 0.05 p < 0.012 2 2 - - 3 3 3 5.606 7.200 2 2 3 4.714 - 3 3 4 5.791 6.746 2 2 4 5.333 - 3 3 5 6.649 7.079 2 2 5 5.160 6.533 3 3 6 5.615 7.410 2 2 6 5.346 6.655 3 3 7 5.620 7.228 2 2 7 5.143 7.000 3 3 8 5.617 7.350 2 2 8 5.356 6.664 3 3 9 5.589 7.422 2 2 9 5.260 6.897 3 3 10 5.588 7.372 2 2 10 5.120 6.537 3 3 11 5.583 7.418 2 2 11 5.164 6.766 3 4 4 5.599 7.144 2 2 12 5.173 6.761 3 4 5 5.656 7.445 2 2 13 5.199 6.792 3 4 6 5.610 7.500 2 3 3 5.361 - 3 4 7 5.623 7.550 2 3 4 5.444 6.444 3 4 8 5.623 7.585 2 3 5 5.251 6.909 3 4 9 5.652 7.614 2 3 6 5.349 6.970 3 4 10 5.661 7.617 2 3 7 5.357 6.839 3 5 5 5.706 7.578 2 3 8 5.316 7.022 3 5 6 5.602 7.591 2 3 9 5.340 7.006 3 5 7 5.607 7.697 2 3 10 5.362 7.042 3 5 8 5.614 7.706 2 3 11 5.374 7.094 3 5 9 5.670 7.733 2 3 12 5.350 7.134 3 6 6 5.625 7.725 2 4 4 5.455 7.036 3 6 7 5.689 7.756 2 4 5 5.273 7.205 3 6 8 5.678 7.796 2 4 6 5.340 7.340 3 7 7 5.688 7.810 2 4 7 5.376 7.321 4 4 4 5.692 7.654 2 4 8 5.393 7.350 4 4 5 5.657 7.760 2 4 9 5.400 7.364 4 4 6 6.681 7.795 2 4 10 5.345 7.357 4 4 7 5.650 7.814 2 4 11 5.365 7.396 4 4 8 5.779 7.853 2 5 5 5.339 7.339 4 4 9 5.704 7.910 2 5 6 5.339 7.376 4 5 5 5.666 7.823 2 5 7 5.393 7.450 4 5 6 5.661 7.936 2 5 8 5.415 7.440 4 5 7 5.733 7.931 2 5 9 5.396 7.447 4 5 8 5.718 7.992 2 5 10 5.420 7.514 4 6 6 5.724 8.000 2 6 6 5.410 7.467 4 6 7 5.706 8.039 2 6 7 5.357 7.491 5 5 5 5.780 8.000 2 6 8 5.404 7.522 5 5 6 5.729 8.028 2 6 9 5.392 7.566 5 5 7 5.708 8.108 2 7 7 5.398 7.491 5 6 6 5.765 8.124 2 7 8 5.403 7.571

Kruskal-Wallis Test Table(for k = 3 and N ≤17)

N = n1+n2+n3 = 17 datak = 3 groups(n1, n2, n3) = (6, 5, 6)(R1, R2, R3) = (38, 69, 46)

)1(3)1(

12

1

2

Nn

R

NNH

k

i i

i

)117(36

46*46

5

69*69

6

38*38

)117(17

12 3

1

2

i i

i

n

R

= 6.609

Since significant points of (p<0.05) and (p<0.01) for (n1, n2, n3) = (6, 5, 6) are 5.765 and 8.124, respectively, there are significant difference(s) somewhere among three groups (p<0.05).

6.6098.1245.765



Q1: Where is significant among A, B, and C?


R1 = R2 = R3 =

1

2

4

7

10

8

11

12

13

3

5

6

9

N = n1+n2+n3 =k =(n1, n2, n3) =(R1, R2, R3) =

)1(3)1(

12

1

2

Nn

R

NNH

k

i i

i

=

Exercise: Kruskal-Wallis TestExercise: Kruskal-Wallis Test

24 44 23

13 samples 3 groups( 5, 4, 4)(24, 44, 23)

6.227

7.7605.657



6.227

There is/are significant difference(s) somewhere among three groups (p<0.05).

Friedman TestFriedman Test2 groups n groups (n > 2)

datadistribution


・ sign test



・ one-way ANOVA

・ two-way ANOVA

(no

norm

ality

) one-waydata

Pa

ram

etr

ic T

est

No

n-p

ara

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Friedman TestFriedman TestWhen (1) more than two groups, (2) data have correspondence (not independent), but (3) the conditions of two-way ANOVA are not satisfied,Let' use ranks of data and Friedman test.

benchmarktasks

methods

a b c d

A 0.92 0.75 0.65 0.81

B 0.48 0.45 0.41 0.52

C 0.56 0.41 0.47 0.50

D 0.61 0.50 0.56 0.54

(ex.) Comparison of recognition rates.a b c d

methods

12

3

4

1

2 3

4 123

4

1

234

Friedman TestFriedman Test

Step 1: Make a ranking table.Step 2: Sum ranks of the factor that you want to test.

Step 3: Calculate the Friedman test value, χ2r .

Step 4: If k =3 or 4, compare χ2r with a significant point

in a Friedman test table. Otherwise, use a χ2 table of (k-1) degrees of freedom.

methods

12

3

4

1

23

4 1 234

123

4

a b c d

)1(3)1(

12

1

22

knRknk

k

iir

where (k, n) are the # of levels of factors 1 and 2.#

of d

ata

(n =

4)

# of methods (k = 4)

benchmarktasks

method

a b c d

A 4 2 1 3 B 3 2 1 4 C 4 1 2 3 D 4 1 3 2

Σ 15 6 7 12 ranking among methods

Example: Friedman TestExample: Friedman TestStep 1: Make a ranking table.Step 2: Sum ranks of the factor that you want to test.


Step 4: Since significant point for (k,n) = (4,4) is7.80, there is/are significant difference(s) somewhere among four methods, a, b, c, and d (p<0.05).

1.8

5*4*31276155*4*4

12

)1(3)1(

12

2222

1

22

knRknk

k

iir

# of

dat

a (n

= 4

)


benchmarktasks

method

a b c d

A 4 2 1 3 B 3 2 1 4 C 4 1 2 3 D 4 1 3 2

Σ 15 6 7 12

k n p<0.05 p<0.01

3

3 6.00 －4 6.50 8.00

5 6.40 8.40

6 7.00 9.00

7 7.14 8.86

8 6.25 9.00 9 6.22 9.56 ∞ 5.99 9.21

4

3 7.40 9.00

4 7.80 9.60

5 7.80 9.96

∞ 7.81 11.34

Friedman test table.

8.19.67.8



Example: Friedman TestExample: Friedman TestStep 1: Make a ranking table.Step 2: Sum ranks of the factor that you want to test.


Step 4: Since significant point for (k,n) = (4,4) is7.80, there is/are significant difference(s) somewhere among four methods, a, b, c, and d (p<0.05).

1.8

5*4*31276155*4*4

12

)1(3)1(

12

2222

1

22

knRknk

k

iir

# of

dat

a (n

= 4

)


benchmarktasks

method

a b c d

A 4 2 1 3 B 3 2 1 4 C 4 1 2 3 D 4 1 3 2

Σ 15 6 7 12

k n p<0.05 p<0.01

3

3 6.00 －4 6.50 8.00

5 6.40 8.40

6 7.00 9.00

7 7.14 8.86

8 6.25 9.00 9 6.22 9.56 ∞ 5.99 9.21

4

3 7.40 9.00

4 7.80 9.60

5 7.80 9.96

∞ 7.81 11.34

Friedman test table.

8.19.67.8



Q1: Where is significant among a, b, c, or d?


Multiple ComparisonsMultiple Comparisons

When there is significant difference among groups, multiple comparison is used to know which group is significantly difference from others.

4C2 = 6 times of pair comparisons with (p < 0.05)

1 - (1 - 0.05)6 = significance level 26.5%!Don't apply multiple pair comparisons simply. Example

Multiple ComparisonsMultiple Comparisons

When there is significant difference among groups, multiple comparison is used to know which group is significantly difference from others.

4C2 = 6 times of pair comparisons with (p < 0.05)

1 - (1 - 0.05)6 = significance level 26.5%!

Solution is to apply multiple pair comparisons with more strict significance level.

Example

Multiple Comparisons

-- Bobferroni method -- Multiple Comparisons

-- Bobferroni method --

When pair comparisons are applied m times,let's use a significance level of p / m.

4C2 = 6 times of pair comparisons with (p < )0.05

6

Features:(1) Simple.(2) Rather strict, i.e. showing significances is rather hard.

Multiple Comparisons

-- Holm method -- Multiple Comparisons

-- Holm method --

Corrected Bonferroni method to detect significances easily.

Example pair comparisons

p-valuecorrected

p-value eqn.corrected

p-value

0.0076 = p-value* 6 0.0456

0.0095 = p-value* 5 0.0475

0.0280 = p-value* 4 0.1120

0.0320 = p-value* 3 0.0960

0.0380 = p-value* 2 0.0760

0.0410 = p-value* 1 0.0410

vs.

vs.

vs.

vs.

vs.

vs.

normality(parametric)


datadistribution

ｔ -test

・ sign test

・ Wilcoxon Signed-Ranks Test

ANOVA(Analysis of Variance)

・ Friedman test

・ kruskal-wallis test

one-way ANOVA

two-way ANOVA

no normality(non-parametric)

one-waydata

two-waydata

Scheffé's Method of Paired ComparisonScheffé's Method of Paired Comparison

＋ Scheffé's method of paired comparison for Human Subjective Tests

IEC

lighting design of 3-D CG

measuring mental scale

geological simulation

hearing-aid fitting

Corridor

W

K

Wall

L

B

Verenda

MEMS design

EvolutionaryComputation

Target System

subjectiveevaluations

Interactive Evolutionary Computation

imag

e en

hanc

emen

t pr

oces

sing

room layoutplanning design

room lighting design byoptimizing LED assignments

??


ANOVA based on nC2 paired comparisons for n objects.

ANOVAevenbetter betterslightly better

slightly better

evenbetter betterslightly better

slightly better


slightly better

significance checkusing a yardstick


Original method and three modified methods

　All subjects must evaluate all pairs.

no yes

　

yes original( 原法 , 1952)

Ura's variation（浦の変法 , 1956)

no Haga's variation（芳賀の変法）

Nakaya's variation（中屋の変法 , 1970)or

der

effe

ct

Order Effect

(1) and then

(2) and then

may result different evaluation.



1. Ask N human subjects to evaluate t objects in 3, 5 or 7 grades. 2. Assign [-1, +1], [-2, +2] or [-3, +3] for these grades.3. Then, start calculation (see other material).


slightly better


slightly better


slightly better

O1 O2 O3 O4 O5 O6

A1 - A2 2 1 1 2 1 2

A1 - A3 2 2 1 1 1 1

A2 - A3 1 0 1 1 -1 0

Six subjects (N = 6)

Pai

red

com

paris

ons

for

t=

3 ob

ject

s.

Questionnaire Total row data

・・・

strap for a mobile phone

invitation to a dinner

tea /coffee stuffed animal fountain pen

Ex. Q.

Application Example:

What is the best present to be her/his boy/girl friend?

[SITUATION] He/he is my longing. I want to be her/his boy/girl friend before we graduate from our university. To get over my one-way love, I decided to present something of about 3,000 JPY and express my heart.

I show you 5C2 pairs of presents. Please compare each pair and mark your relative evaluation in five levels.

・・

・・

present from a male

(significant difference)

Results of Scheffé's Method of Paired Comparison (Nakaya's variation)

What is the best present to be her/his boy/girl friend?

present from a female

I th

ink

effe

ctiv

e.R

ealit

y is

...

-1 　 -0.5 　 0 　 0.5 1

I will catch her heart by dinner.

mor

eef

fect

ive

less

effe

ctiv

e

-1 　 -0.5 　 0 0.5 1

How about tea leave or a stuffed anima?

mor

eef

fect

ive

less

effe

ctiv

e

-1 -0.5 0 0.5 1

Eat! Eat! Eat!

mor

eef

fect

ive

less

effe

ctiv

e

-1 　 -0.5 　 0 0.5 1

mor

eef

fect

ive

less

effe

ctiv

e

I hesitate to accept it as we have not gone about with him.



no yes

　





der

effe

ct

Scheffé's Method of Paired Comparison

Modified methods by Ura and NakayaScheffé's Method of Paired Comparison

Modified methods by Ura and Nakaya


Modified method by UraScheffé's Method of Paired Comparison

Modified method by UraPairwise comparisons for objects which are effected by display order (order effect).


slightly better


slightly better


slightly better


slightly better


slightly better


slightly better

-2 -1 0 1 2

-2 -1 0 1 2

-2 -1 0 1 2

-2 -1 0 1 2

-2 -1 0 1 2

-2 -1 0 1 2


slightly better


slightly better


slightly better


slightly better


slightly better


slightly better

-2 -1 0 1 2

-2 -1 0 1 2

-2 -1 0 1 2

-2 -1 0 1 2

-2 -1 0 1 2

-2 -1 0 1 2



Modified method by UraAsk N human subjects to evaluate 2×tC2 pairs for t objects in 3, 5 or 7 grades and assign [-1, +1], [-2, +2] or [-3, +3], respectively.



Modified method by Ura

Step 1: Make paired comparison table of each human subject.


slightly better

-2 -1 0 1 2

-2 -1 0 1 2

-2 -1 0 1 2

-2 -1 0 1 2

-2 -1 0 1 2

-2 -1 0 1 2

A1

A2

A1

A1

A2

A4

A4

A3

A3

A2

A4

A3

・・・・・・・・・

A1 A2 A3 A4

A1 0 -1 -1

A2 3 0 0

A3 3 1 -1

A4 3 3 1

ijlx : evaluation value when the l-th human subject compares the i-th object with the j-th object.

SubjectO1

Subject O3

SubjectO2




Step 1: Make paired comparison table of each human subject.

)(2

1ˆ iii xx

tN

-1.1667 0.5417-0.5000 1.1250

A4 A3 A2 A1

1̂2̂3̂4̂

Average of four objects

where t: # of object (4) N: # of human subjects (3)



Modified method by UraStep 2: Make a table summing all subjects' data and calculate the average evaluations for all objects.

27 13 -12 -28

SxxN

S

Sxxt

S

xxtN

S

i ijjiij

l iilliB

iii

2

2)(

2

)(2

1

)(2

1

)(2

1

l i ijijlT

BBT

ilB

xS

SSSSSSS

Sxtt

S

xtNt

S

2

)()(

2)(

2

)1(

1

)1(

1

freedom. of degree and

,,,,,, where )()(

f

SSSSSSSS TBB



Modified method by UraStep 3: Make a ANOVA table.

unbiased variance = S/f

F = unbiased variance

unbiased variance of S

for F tests.

SxxN

S

Sxxt

S

xxtN

S

i ijjiij

l iilliB

iii

2

2)(

2

)(2

1

)(2

1

)(2

1

l i ijijlT

BBT

ilB

xS

SSSSSSS

Sxtt

S

xtNt

S

2

)()(

2)(

2

)1(

1

)1(

1




ANOVA table.

-1.1667 0.5417-0.5000 1.1250

A4 A3 A2 A1

1̂2̂3̂4̂There are significant difference among A1 - A4




ANOVA table.



Modified method by UraStep 4: Apply multiple comparisons.

Q1: Where is significant among A1, A2, and A3?

A1: Apply multiple comparisons between all pairs.(Fisher's PLSD method, Scheffé method, Bonferroni-Dunn test, Dunnett method, Williams method, Tukey method, Nemenyi test, Tukey-Kramer method, Games/Howell method, Duncan's new multiple range test, Student-Newman-Keuls method, etc. Each has different characteristics.)

Step 4: Apply multiple comparisons between all pairs and find which distance is significant.

Example of a simple multiple comparison.• Calculate a studentized yardstick • When a difference of average > a studentized

yardstick, the distance is significant.




-1.1667 0.5417-0.5000 1.1250

A4 A3 A2 A1

1̂2̂3̂4̂

(Fisher's PLSD method, Scheffé method, Bonferroni-Dunn test, Dunnett method, Williams method, Tukey method, Nemenyi test, Tukey-Kramer method, Games/Howell method, Duncan's new multiple range test, Student-Newman-Keuls method, etc. Each has different characteristics.)

(See in the next slide.)




tNftqY 2/ˆ),( 2

Step 4: Example of a simple multiple comparisons.

(studentized yardstick)

When (t, f) = (4,21), studentized yardsticks for significance levels of 5% and 1% are:

),,ˆ( 2 Ntwhere are an unbiased variance of Sε, the # of objects, and the #ofhuman subjects; is a studentized range obtained is a statistical test table for t, the degree of freedom of Sε ( ), and the significant level of φ; see these variables in an ANOVA table.

),( ftqf

)21,4(05.0q

Studentized yardstick ),(05.0 ftq　 2 3 4 5 6 7 8 9 10 12 15 20

1 18.0 27.0 32.8 37.1 40.4 43.1 45.4 47.4 49.1 52.0 55.4 59.6

2 6.09 8.30 9.80 10.9 11.7 12.4 13.0 13.5 14.0 14.7 15.7 16.8

3 4.50 5.91 6.82 7.50 8.04 8.48 8.85 9.18 9.46 9.95 10.5 11.2

4 3.93 5.04 5.76 6.29 6.71 7.05 7.35 7.60 7.83 8.21 8.66 9.23

5 3.64 4.60 5.22 5.67 6.03 6.33 6.58 6.80 6.99 7.32 7.72 8.21

6 3.46 4.34 4.90 5.31 5.63 5.89 6.12 6.32 6.49 6.79 7.14 7.59

7 3.34 4.16 4.68 5.06 5.36 5.61 5.82 6.00 6.16 6.43 6.76 7.17

8 3.26 4.04 4.53 4.89 5.17 5.40 5.60 5.77 5.92 6.18 6.48 6.87

9 3.20 3.95 4.42 4.76 5.02 5.24 5.43 5.60 5.74 5.98 6.28 6.64

10 3.15 3.88 4.33 4.65 4.91 5.12 5.30 5.46 5.60 5.83 6.11 6.47

11 3.11 3.82 4.26 4.57 4.82 5.03 5.20 5.35 5.49 5.71 5.99 6.33

12 3.08 3.77 4.20 4.51 4.75 4.95 5.12 5.27 5.40 5.62 5.88 6.21

13 3.06 3.73 4.15 4.45 4.69 4.88 5.05 5.19 5.32 5.53 5.79 6.11

14 3.03 3.70 4.11 4.41 4.67 4.83 4.99 5.10 5.25 5.46 5.72 6.03

15 3.01 3.67 4.08 4.37 4.60 4.78 4.94 5.08 5.20 5.40 5.66 5.96

16 3.00 3.65 4.05 4.33 4.56 4.74 4.90 5.03 5.15 5.35 5.59 5.90

17 2.98 3.63 4.02 4.30 4.52 4.71 4.86 4.99 5.11 5.31 5.55 5.84

18 2.97 3.61 4.00 4.28 4.49 4.67 4.82 4.96 5.07 5.27 5.50 5.79

19 2.96 3.59 3.98 4.25 4.47 4.65 4.79 4.92 5.04 5.23 5.46 5.75

20 2.95 3.58 3.96 4.23 4.45 4.62 4.77 4.90 5.01 5.20 5.43 5.71

24 2.92 3.53 3.90 4.17 4.37 4.54 4.68 4.81 4.92 5.10 5.32 5.59

30 2.89 3.49 3.84 4.10 4.30 4.46 4.60 4.72 4.83 5.00 5.21 5.48

40 2.86 3.44 3.79 4.04 4.23 4.39 4.52 4.63 4.74 4.91 5.11 5.36

60 2.83 3.40 3.74 3.98 4.16 4.31 4.44 4.55 4.65 4.81 5.00 5.24

120 2.80 3.36 3.69 3.92 4.10 4.24 4.36 4.48 4.56 4.72 4.90 5.13

∞ 2.77 3.31 3.63 3.86 4.03 4.17 4.29 4.39 4.47 4.62 4.80 5.01

f t



Modified method by UraStep 4: Example of a simple multiple comparisons.



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der

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ct


Modified methods by Ura and NakayaScheffé's Method of Paired Comparison

Modified methods by Ura and Nakaya


slightly better


slightly better


slightly better

-2 -1 0 1 2

-2 -1 0 1 2

-2 -1 0 1 2


Modified method by NakayaScheffé's Method of Paired Comparison

Modified method by NakayaPairwise comparisons for objects that can be compared without order effect.



Modified method by Nakaya1. Ask N human subjects to evaluate t objects in 3, 5 or 7 grades. 2. Assign [-1, +1], [-2, +2] or [-3, +3] for these grades, respectively.3. Then, start calculation (see other material).

O1 O2 O3 O4 O5 O6

A1 - A2 2 3 3 2 0 1

A1 - A3 2 0 0 1 1 0

A2 - A3 -3 -2 -1 -1 -3 -2

Six human subjects (N = 6)

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Questionnaire


slightly better


slightly better


slightly better

-2 -1 0 1 2

-2 -1 0 1 2

-2 -1 0 1 2



Modified method by NakayaStep 1: Make paired comparison table of each human subject.

: evaluation value when the l-th human subject compares the i-th object with the j-th object. ijlx

ii xtN

1̂Average of four objects

where t: # of object 　 (3) N: # of human subjects (6)



Modified method by NakayaStep 2: Make a table summing all subjects' data and calculate the average evaluations for all objects.

ii

l iliB

ii

xtN

S

Sxt

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2

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1

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1

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of varianceUnbariased

varianceUnbiased

1

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There are significant difference among A1 - A3



Modified method by NakayaStep 3: Make a ANOVA table.

ANOVA table.



Modified method by NakayaStep 4: Apply multiple comparisons.

Q1: Where is significant among A1, A2, and A3?


ANOVA table.



Modified method by NakayaStep 4: Apply multiple comparisons between all pairs and find which distance is significant.

Example of a simple multiple comparison.• Calculate a studentized yardstick • When a difference of average > a studentized

yardstick, the distance is significant.

(Fisher's PLSD method, Scheffé method, Bonferroni-Dunn test, Dunnett method, Williams method, Tukey method, Nemenyi test, Tukey-Kramer method, Games/Howell method, Duncan's new multiple range test, Student-Newman-Keuls method, etc. Each has different characteristics.)

1980.263/79.197.6

4506.163/79.160.4

01.0

05.0

Y

Y



Modified method by NakayaStep 4: Example of a simple multiple comparisons.

tNftqY /ˆ),( 2 (studentized yardstick)

),,ˆ( 2 Ntwhere are an unbiased variance of Sε, the # of objects, and the #ofhuman subjects; is a studentized range obtained is a statistical test table for t, the degree of freedom of Sε ( ), and the significant level of φ; see these variables in an ANOVA table.

),( ftqf

(See in the next slide.))5,3(05.0q

Studentized yardstick ),(05.0 ftq　 2 3 4 5 6 7 8 9 10 12 15 20

1 18.0 27.0 32.8 37.1 40.4 43.1 45.4 47.4 49.1 52.0 55.4 59.6

2 6.09 8.30 9.80 10.9 11.7 12.4 13.0 13.5 14.0 14.7 15.7 16.8

3 4.50 5.91 6.82 7.50 8.04 8.48 8.85 9.18 9.46 9.95 10.5 11.2

4 3.93 5.04 5.76 6.29 6.71 7.05 7.35 7.60 7.83 8.21 8.66 9.23

5 3.64 4.60 5.22 5.67 6.03 6.33 6.58 6.80 6.99 7.32 7.72 8.21

6 3.46 4.34 4.90 5.31 5.63 5.89 6.12 6.32 6.49 6.79 7.14 7.59

7 3.34 4.16 4.68 5.06 5.36 5.61 5.82 6.00 6.16 6.43 6.76 7.17

8 3.26 4.04 4.53 4.89 5.17 5.40 5.60 5.77 5.92 6.18 6.48 6.87

9 3.20 3.95 4.42 4.76 5.02 5.24 5.43 5.60 5.74 5.98 6.28 6.64

10 3.15 3.88 4.33 4.65 4.91 5.12 5.30 5.46 5.60 5.83 6.11 6.47

11 3.11 3.82 4.26 4.57 4.82 5.03 5.20 5.35 5.49 5.71 5.99 6.33

12 3.08 3.77 4.20 4.51 4.75 4.95 5.12 5.27 5.40 5.62 5.88 6.21

13 3.06 3.73 4.15 4.45 4.69 4.88 5.05 5.19 5.32 5.53 5.79 6.11

14 3.03 3.70 4.11 4.41 4.67 4.83 4.99 5.10 5.25 5.46 5.72 6.03

15 3.01 3.67 4.08 4.37 4.60 4.78 4.94 5.08 5.20 5.40 5.66 5.96

16 3.00 3.65 4.05 4.33 4.56 4.74 4.90 5.03 5.15 5.35 5.59 5.90

17 2.98 3.63 4.02 4.30 4.52 4.71 4.86 4.99 5.11 5.31 5.55 5.84

18 2.97 3.61 4.00 4.28 4.49 4.67 4.82 4.96 5.07 5.27 5.50 5.79

19 2.96 3.59 3.98 4.25 4.47 4.65 4.79 4.92 5.04 5.23 5.46 5.75

20 2.95 3.58 3.96 4.23 4.45 4.62 4.77 4.90 5.01 5.20 5.43 5.71

24 2.92 3.53 3.90 4.17 4.37 4.54 4.68 4.81 4.92 5.10 5.32 5.59

30 2.89 3.49 3.84 4.10 4.30 4.46 4.60 4.72 4.83 5.00 5.21 5.48

40 2.86 3.44 3.79 4.04 4.23 4.39 4.52 4.63 4.74 4.91 5.11 5.36

60 2.83 3.40 3.74 3.98 4.16 4.31 4.44 4.55 4.65 4.81 5.00 5.24

120 2.80 3.36 3.69 3.92 4.10 4.24 4.36 4.48 4.56 4.72 4.90 5.13

∞ 2.77 3.31 3.63 3.86 4.03 4.17 4.29 4.39 4.47 4.62 4.80 5.01

f t

SUMMARYSUMMARY


datadistribution


・ sign test・ Wilcoxon signed-ranks test ・ Friedman test


・ one-way ANOVA

・ two-way ANOVA

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data

two-waydata

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Scheffé's method of paired comparison for Human Subjective Tests

1. We overview which statistical test we should use for which case.

2. We can appeal the effectiveness of our experiments with correct use of statistical tests.

statistical tests for computational intelligence research and human subjective tests

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