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 Use simultaneous confidence intervals on preplanned comparisons Are the treatments unstructured? Use appropriate multiple comparison tests (today’s topic) 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” Comparison of Means Pairwise Comparisons – Least Significant Difference (LSD) Simultaneous Confidence Intervals – Tukey’s Honestly Significant Difference (HSD) – Dunnett Test (making all comparisons to a control) • May be a one-sided or two-sided test – Bonferroni Inequality – Scheffé’s Test – can be used for unplanned comparisons Other Multiple Comparison Tests - “Data Snooping” – – – – Fisher’s Protected LSD (FPLSD) Student-Newman-Keuls test (SNK) 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 Type I vs Type II Errors Type I error - saying something is different when it is really the same (false positive) (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 (false negative) (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 to make all possible comparisons – the chance for a Type I error increases dramatically as the number of treatments increases Pairwise Comparisons Making all possible pairwise comparisons among t treatments – # of comparisons: t! t(t 1) t 2 2!(t 2)! 2 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 quite a few more than t-1 df = 9 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 – Also called familywise error rate (FWE) 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 if t = 10, Max x = 45 E = 1-(1-0.05)45 = 90% Comparisonwise error rate C = 1- (1-E)1/x Least Significant Difference Calculating a t for testing the difference between two means t calc (Y1 Y2 ) / s 2Y1 Y2 – Any difference for which the tcalc > 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 – For equal replication, where r is the number of observations forming each mean LSD t 2 * MSE 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 test for treatments is significant**) be used for – Making all possible pairwise comparisons – Making more comparisons than df for treatments **Some would say that LSD should never be used unless the F test from ANOVA is significant Pick the Winner A plant breeder wanted to measure resistance to stem rust for six wheat varieties – – – – 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 Yi1 - 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% LSD t 0.05,df 18 2 * MSE 2 *120 2.101 16.27 r 4 LSD t 0.01,df 18 2 * MSE 2 *120 2.878 22.29 r 4 LSD=0.05 = 16.27 LSD=0.01 = 22.29 Back to the data... Mean Yield Difference Yi1 - 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** 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 t 2 * MSE r So in our example data, any difference between means that is greater than 16.27 is declared to be significant Tukey’s Honestly Significant Difference (HSD) From a table of Studentized range values (see handout), select a value of Q which depends on p (the number of means) and v (error df) Compute: HSD Q,p,v MSE r For any pair of means, if the difference is greater than HSD, it is significant Uses an experimentwise error rate Use the Tukey-Kramer test with unequal sample size HSD Q,p,v MSE 1 1 2 r1 r2 Student-Newman-Keuls Test (SNK) Rank the means from high to low Compute t-1 significant differences, SNKj , using the studentized values for the HSD SNK j Q,k,v MSE r where j=1,2,..., t-1; k=2,3,...,t k = number of means in the range 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 Uses comparisonwise for adjacent means 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 F 1 Yi 5 4 3 = 15 comparisons 95.3 18 df for error D 2 94.0 E 3 75.0 B 4 69.0 MSE 120 se 5.477 r 4 A 5 50.3 SNK=Q*se C 6 24.0 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 Bonferroni Inequality Theory E X * C where X = number of pairwise comparisons To get critical probability value for significance C = E / X where E = maximum desired experimentwise error rate Alternatively, multiply observed probability value by X and compare to E (values >1 are set to 1) Advantages – simple – strict control of Type I error Disadvantage – very conservative, low power to detect differences False Discovery Rate Bars show P values for simple t tests among means – Largest differences have the smallest P values Line represents critical P values = (i/X)* E False Positive Procedure 0.25 Probability 0.20 0.15 0.10 i = 1 to X Ranks for Reject H0 0.05 Yi -Yj 0.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 Rank (i) More Options! Х Duncan’s New Multiple Range Test – A multiple range test – Less conservative than the SNK test – Used to be popular, but no longer recommended Dunnett’s Test – Compare all treatments against a control – Compare all treatments against the best treatment – Conservative (controls Type 1, not Type 2 error) Scheffe’s Test – Can be used for comparisons that are not preplanned – Very conservative! 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 (FDR) has nice features – Good experimentwise Type I error control – Good power (Type II error control) – May not be as well-known as some other tests 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