Hi, Wei
Sharing few fundamental ponits, hope these will be of some help.
As a standard procedure, best to carry out a “Descriptive Statistics” on each data set.
1. “Descriptive Statistics”
(1a) Anderson-Darling Normality Test.
(1b) P value (higher then 0.05 is normal).
(1c) mean, sd and many more data output post analysis.
(1d) Boxplot
(1e) 95% CI for Mu and 95% CI for median
Via this approach – a snap shot of your each data set is obtained. Then this should allow you to plan next steps, what further tests are suitable. Mike, comments on Normality is cover with this approach and also comments made by othe contributors.
2. Type of T-tests.
(2a) Paired T-test: I have successful applied these when comparing old vs new equipment during the capital prog of puchase. For example, same sample two different equipment.
(2b) Two Sample T-test: I have successfully applied these – raw material vs supplier’s. For example same raw material vs different supplier’s.
3 “Test for Equal Variance”.
This will give F-test (simple terms comparing sd) and Leavene’s test – these are for normal distribution and not normal distribution, respectively. P value is given.
4. ANOVA – this also available. But, I usually go for ANOVA for data set more then 2.
5. Have you considered the Gage and R&R, to enusre the data variability is understood – repeatability or reproducibility . What is part-to-part varition?
Finally, if your data sets is comparison of two – then T-test and Test for Equal Variance will be valuable. However, without fully understanding the data type and n (no of data points) – hence a generic comments.
If you need further comments, contact via my email.
Ali
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