hypothesis testing roadmap 1
TRANSCRIPT
7/27/2019 Hypothesis Testing Roadmap 1
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Hypothesis tests are typically used in the Analyze phase to identify the critical x’s (inputs) for a process. Generally, these critical x’s are assumed to exist when
null hypothesis. The significance level ( α or alpha) is typically set at 95% or p-value = 0.05.Six SigmaHypothesis Testing Using Minitab
Two Samplet-Test
Friedman
What typeof data doyou have?
How many
samplesare youtesting?
Levene’s Test
Mann-Whitney
1-SampleSign
Paired t-Test
One Samplet-Test
Bartlett’s Test
KruskallWallis
F-Test
Are the
variancesequal?
Do youhave morethan onesample?
MoodsMedian Test
Ho: σ1 = σ2 = σ3 ...Ha: at least one is differentData: stacked onlyStat>ANOVA>Test for
Equal Varianceuse Levene’s statistics
onlytwo
two ormore
Two Samplet-Test
One SampleProportion
Test
One WayANOVA
One-SampleWilcoxon
yes no
Ho: ŋ1 = ŋ 2
Ha: ŋ1 ≠ ŋ 2
(where ŋ is thepopulation median)
Data: unstacked onlyStat>Nonparametrics>
Mann-Whitneysee Note 2
no
If your data is not normally distributed, you shouldanalyze the distribution (first look at its shape);consider using: Box Cox transformation
Stat>Control Charts>Box Cox Transformation… EDA macro & brush outliers
Editor>Enable Commands (Session windowactive),type %EDA (column reference)
Attempt to fit the curveStat>Reliability/Survival>(pick one)
etc.
Ho: all treatmenteffects are zero
Ha: not all treatmenteffects are zero
Data: stacked onlyStat>Nonparametrics>
Friedmansee Note 2
Ho: all of the populationmedians are equal
Ha: the medians are not allequal
Data: stacked onlyStat>Nonparametrics>
Kruskal-Wallissee Note 2more powerful thanMoods for manydistributions -except
outliers
Ho: all of t he populationmedians are equal
Ha: the medians are notall equal
Data: stacked onlyStat>Nonparametrics>Mood’s Median Test
see Note 2better than KruskallWallis for handlingoutliers
Two SampleProportion
Test
C
How manysamples?
nonparametricmethods
parametricmethods
yes
one
two (2)
yes
no
Is the datanormally
distributed?
continuous(variable)
Start
attribute(discrete)
How manysamples?
morethan 2
Ho: σ1 = σ2
Ha: σ1 ≠ σ2
Data: unstacked or stacked
Stat>Basic Statistics>2 Variances
Ho: σ1 = σ2 = σ3….Ha: at least one is differentData: unstackedStat>ANOVA>Test for
Equal Varianceuse Bartlett’s statistics
(F-test if only 2 samples)
two ( 2)
one
Ho: p = p0
Ha: p ≠ p0
(where p is the populationproportion and p0 is thehypothesized value)
Data: stacked or unstackedStat>Basic Statistics>
1 Proportion
Ho: pHa: aDataStat
Te
H0: p1 - p2 = p0
Ha: p1 - p2 ≠ p0
(where p1 and p2 are the samproportions and p0 is thehypothesized difference)
Data: unstacked or stackedStat>Tables>Chi Square Te
Ho: median =hypothesized median
Ha: median ≠hypothesized median
Data: stacked or unstacked
Stat>Nonparametrics>1-Sample Sign
Ho: median = hypothesized medianHa: median ≠ hypothesized medianData: stacked or unstackedStat>Nonparametrics>
1-Sample Wilcoxonassumes data are a random samplefrom a continuous, symmetricpopulation
Ho: μ = μ0
Ha: μ ≠ μ0
(where μ is the populationmean and μ0 is thehypothesized mean)
Data: unstackedStat>Basic Statistics>
1-Sample t
Ho: μ1 – μ2 = δ0
Ha: μ1 – μ2 ≠ δ0
(where μ1 and μ2 represent populationmeans and δ0 the hypothesized difference)
Data: stacked or unstackedStat>Basic Statistics>
2-Sample t Assume Equal Variances (do not check)see Note 1use (vs. Paired t-Test) when samples aredrawn independently from two populations
Ho: μd = μ0
Ha: μd ≠ μ0
(where μd represents thepopulation mean of thedifferences and μ0 thehypothesized mean)
Data: unstacked onlyStat>Basic Statistics>Paired tSee Note 1
Ho: μ1 – μ2 = δ0
Ha: μ1 – μ2 ≠ δ0
(where μ1 and μ2 represent population meansand δ0 the hypothesized difference)
Data: stacked or unstackedStat>Basic Statistics>
2-Sample t
Assume Equal Variances (check)
Ho: μ1 = μ2 = μ3…Ha: at least one is differentData: stackedStat>ANOVA>One-way
for unstacked data use:Stat>ANOVA>One-way(Unstacked)
v a r i a n c
m e a n / m e d i a n / p r o p o r t i o n
Rev:
Are the
variancesequal?
yes
Evaluate samplestwo-at-a-time using
t-test
no
NOTE: Remember to evaluateyour sample size requirements
β is usually set at 10% for test of means:Stat>Power and SampleSize>(appropriate test)
NOTE: Reyour samp
β is usu for test Stat>PoSize>(a
NOTE: Nonparametric tests generally requirelarger sample sizes to discern the samedifference (e.g., 10 minutes between 2 cycletime medians vs. 10 minutes between 2averages). As a general rule of thumb, use100% to 115% of the s ample size computed in
Minitab for the comparable parametric test(see also: Asymptotic Relative Efficiency(ARE) or Pitman efficiency).
Note 1The hypothesis tests for the Paired t-test (H o: μ 1 -μ2 = 0) and Two Sample t-test (H o: μ1 = μ2) shownin Minitab are different than those traditionallyshown. Note that the default Test Mean in Minitabfor the Two Sample t-test and Paired t-test is 0 andcan be user-defined under using the Optionsbutton.Note 2Generally, the nonparametric median tests assumethat the distributions are the same (e.g., sample 1and sample 2 are both right-skewed).
For all of the h ypothesis tests: p-value ≥ 0.05 – fail to reject H0
p-value < 0.05 – reject H0