Transcript Mean Comparison Tests - Crop and Soil Science

Treatment comparisons
 ANOVA can determine if there are differences
among the treatments, but what is the nature of
those differences?
 Are the treatments measured on a continuous scale?
 Look at response surfaces (linear regression,
polynomials)
 Is there an underlying structure to the treatments?
 Compare groups of treatments using orthogonal
contrasts or a limited number of preplanned mean
comparison tests
 Are the treatments unstructured?
 Use appropriate mean comparison tests
Comparison of Means
 Pairwise Comparisons
– Least Significant Difference (LSD)
 Simultaneous Confidence Intervals
– Dunnett Test (making all comparisons to a control)
– Bonferroni Inequality
 Other Multiple Comparisons - “Data Snooping”
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–
–
–
–
Fisher’s Protected LSD (FPLSD)
Student-Newman-Keuls test (SNK)
Tukey’s honestly significant difference (HSD)
Waller and Duncan’s Bayes LSD (BLSD)
False Discovery Rate Procedure
 Often misused - intended to be used only for data from
experiments with unstructured treatments
Multiple Comparison Tests
 Fixed Range Tests – a constant value is used
for all comparisons
– Application
• Hypothesis Tests
• Confidence Intervals
 Multiple Range Tests – values used for
comparison vary across a range of means
– Application
• Hypothesis Tests
Variety Trials
 In a breeding program, you need to examine
large numbers of selections and then narrow to
the best
 In the early stages, based on single plants or
single rows of related plants. Seed and space
are limited, so difficult to have replication
When numbers have been reduced
and there is sufficient seed, you can
conduct replicated yield trials and
you want to be able to “pick the
winner”
Least Significant Difference
 Calculating a t for testing the difference between
two means
t = (Y1  Y2 ) / s 2Y1 Y2
– any difference for which the t > t would be declared
significant
2
t
s
 Further,  Y1  Y2
is the smallest difference for
which significance would be declared
– therefore
LSD = t  s 2Y1  Y2
– or with equal replication,
where r is number of observations forming the mean
LSD = t  2MSE / r
Do’s and Don’ts of using LSD
 LSD is a valid test when
– making comparisons planned in advance of seeing
the data (this includes the comparison of each
treatment with the control)
– Comparing adjacent ranked means
 The LSD should not (unless F for treatments is
significant) be used for
– making all possible pairwise comparisons
– making more comparisons than df for treatments
Pick the Winner
 A plant breeder wanted to measure resistance to
stem rust for six wheat varieties
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–
planted 5 seeds of each variety in each of four pots
placed the 24 pots randomly on a greenhouse bench
inoculated with stem rust
measured seed yield per pot at maturity
Ranked Mean Yields (g/pot)
Mean Yield
Difference
Yi-1 - Yi
Variety
Rank
Yi
F
1
95.3
D
2
94.0
1.3
E
3
75.0
19.0
B
4
69.0
6.0
A
5
50.3
18.7
C
6
24.0
26.3
ANOVA
Source
df
MS
Variety
5
2,976.44
18
120.00
Error
F
24.80
 Compute LSD at 5% and 1%
LSDt  =
2MSE / r = 2.101 (2 * 120) / 4 = 16.27
LSD = t  2MSE / r = 2.878 (2 * 120) / 4 = 22.29
Back to the data...
LSD=0.05 = 16.27
LSD=0.01 = 22.29
Mean Yield
Difference
Yi-1 - Yi
Variety
Rank
Yi
F
1
95.3
D
2
94.0
1.3
E
3
75.0
19.0*
B
4
69.0
6.0
A
5
50.3
18.7*
C
6
24.0
26.3**
Pairwise Comparisons
 If you have 10 varieties and want to look at all
possible pairwise comparisons
– that would be t(t-1)/2 or 10(9)/2 = 45
– that’s a few more than t-1 df = 9
 LSD would only allow 9 comparisons
Type I vs Type II Errors
 Type I error - saying something is different when it is really the
same (Paranoia)
– the rate at which this type of error is made is the significance
level
 Type II error - saying something is the same when it is really
different (Sloth)
– the probability of committing this type of error is designated b
– the probability that a comparison procedure will pick up a real
difference is called the power of the test and is equal to 1-b
 Type I and Type II error rates are inversely related to each other
 For a given Type I error rate, the rate of Type II error depends on
– sample size
– variance
– true differences among means
Nobody likes to be wrong...
 Protection against Type I is choosing a significance level
 Protection against Type II is a little harder because
– it depends on the true magnitude of the difference
which is unknown
– choose a test with sufficiently high power
 Reasons for not using LSD for more than t-1
comparisons
– the chance for a Type I error increases dramatically as
the number of treatments increases
– for example, with only 20 means - you could make a
type I error 95% of the time (in 95/100 experiments)
Comparisonwise vs Experimentwise Error
 Comparisonwise error rate ( = C)
– measures the proportion of all differences that are
expected to be declared real when they are not
 Experimentwise error rate (E)
– the risk of making at least one Type I error among the
set (family) of comparisons in the experiment
– measures the proportion of experiments in which one
or more differences are falsely declared to be
significant
– the probability of being wrong increases as the number
of means being compared increases
Comparisonwise vs Experimentwise Error
 Experimentwise error rate (E)
Probability of no Type I errors = (1-C)x
where x = number of pairwise comparisons
Max x = t(t-1)/2 , where t=number of treatments
 Probability of at least one Type I error
E = 1- (1-C)x
 Comparisonwise error rate
C = 1- (1-E)1/x
if t = 10, Max x = 45, E = 90%
Fisher’s protected LSD (FPLSD)
 Uses comparisonwise error rate
 Computed just like LSD but you don’t use it
unless the F for treatments tests significant
LSD = tα 2MSE / r
 So in our example data, any difference between
means that is greater than 16.27 is declared to
be significant
Waller-Duncan Bayes LSD (BLSD)
 Do ANOVA and compute F (MST/MSE) with q and f df
(corresponds to table nomenclature)
 Choose error weight ratio, k
– k=100 corresponds to 5% significance level
– k=500 for a 1% test
 Obtain tb from table (A7 in Petersen)
– depends on k, F, q (treatment df) and f (error df)
 Compute
BLSD = tb 2MSE/r
 Any difference greater than BLSD is significant
 Does not provide complete control of experimentwise Type
I error
 Reduces Type II error
Duncan’s New Multiple-Range Test
 Alpha varies depending on the number of means
involved in the test
Alpha
0.05
Error Degrees of Freedom
6
Error Mean Square
113.0833
Number of Means
Critical Range
2
26.02
3
26.97
4
27.44
5
27.67
Means with the same letter are not significantly different.
Duncan Grouping
Mean
N
variety
A
A
A
A
A
A
A
95.30
2
6
94.00
2
4
75.00
2
5
69.00
2
2
50.30
2
1
22.50
2
3
B
B
B
B
B
C
6
27.78
Student-Newman-Keuls Test (SNK)
 Rank the means from high to low
 Compute t-1 significant differences, SNKj , using the
HSD
SNK j = Q,k, MSE / r where j=1,2,..., t-1, k=2,3,...,t
 Compare the highest and lowest
– if less than SNK, no differences are significant
– if greater than SNK, compare next highest mean with
next lowest using next SNK
 Uses experimentwise for the extremes and
comparisonwise for adjacent
Using SNK with example data:
k
2
3
4
5
6
Q
2.97
3.61
4.00
4.28
4.49
19.77 21.91
23.44
24.59
2
1
SNK 16.27
Mean Yield
Variety Rank
Yi
F
D
1
2
5
4
3
= 15 comparisons
95.3
94.0
E
3
75.0
B
4
69.0
A
5
50.3
C
6
24.0
18 df for error
se=
MSE / r = SQRT(120/4) = 5.477
SNK=Q*se
Tukey’s honestly significant difference (HSD)
 From a table of studentized range values, select
a value of Q which depends on k (the number
of means) and v (error df) (Appendix Table VII in
Kuehl)
 Compute HSD as
HSD = Q MSE / r
 For any pair of means, if the difference is greater
than HSD, it is significant
 Uses an experimentwise error rate
 Dunnett’s test is a special case where all
treatments are compared to a control
Bonferroni Inequality
E  x * C
where x = number of pairwise comparisons
C = E / x
where E = maximum desired experimentwise error rate
 Advantages
– simple
– strict control of Type I error
 Disadvantage
– very conservative, low power to detect differences
False Discovery Rate
False Positive Procedure
0.25
Probability
0.20
0.15
Reject H0
0.10
0.05
0.00
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Rank
Most Popular
 FPLSD test is widely used, and widely abused
 BLSD is preferred by some because
– It is a single value and therefore easy to use
– Larger when F indicates that the means are homogeneous and
small when means appear to be heterogeneous
 The False Discovery Rate has nice features, but
is it widely accepted in the literature?
 Tukey’s HSD test
– widely accepted and often recommended by statisticians
– may be too conservative if Type II error has more serious
consequences than Type I error