Yan D. ZhaoEli Lilly and CompanyShuguang HuangWyethInternational journal of industrial and systems engineeringRahardja, D., Zhao, Y.D. and Huang, S. (2009) `Statistical analysis for comparing two 2D-part fixtures', International J. of Industrial and Systems Engineering, Vol. 4, No. 2, pp.216-228.
The T-test (aka Student’s T-test) is a tool for comparing two data groups which have different mean values. The T-test allows the user to interpret whether differences are statistically significant or merely coincidental. For example, do women and men have different mean heights? We can te...
Which inferential statistical test is used to compare the means of 2 groups? a. ANOVA b. chi-square c. t-test Selection of Inferential Statistics As the name suggests the inferential statistics is about drawing inferences about a population characteristic ba...
First, we created a null data set based on the HCA-BM data (Methods). Briefly, we first randomly partitioned the eight HCA-BM samples into two groups and removed the group differences to create a dataset where we do not expect any XDE genes between the two groups (Fig.4a). WhenLamian...
Note that this approach can be easily transferred to a two-sample test scenario. In this case, the input into LISA is two groups of contrast maps. In each random permutation, every contrast map is randomly assigned to one of the two groups. The rest of the analysis proceeds as before. ...
T-tests are used when comparing the means of precisely two groups (e.g., the average heights of men and women). ANOVA and MANOVA tests are used when comparing the means of more than two groups (e.g., the average heights of children, teenagers, and adults)....
For equivalence and noninferiority studies, report the largest difference between groups that will still be accepted as indicating biological equivalence (the equivalence margin). 5. Identify the name of the test used in the analysis. 6. Report whether the test was one- or two-tailed and for ...
regardless of direction, you'll want to use a two-tailed test. This is often the default choice in research when you want to ensure that your analysis captures any significant findings, whether they go in the expected direction or not. Here are examples of hypotheses suited for a two-tailed...
and there are too many possible safety endpoints to be able to control type I error while preserving power. Safety analysis tends to be somewhat ad hoc and exploratory. But with the large quantity of safety data acquired during clinical drug testing, safety data are rarely harvested to their ...
Statistical significance is a conclusion that a set of data is not the result of chance but can instead be attributed to a specific cause. Statistical significance is vital for professionals in any field that relies on the analysis of data, including economics, finance, investing, medicine, physi...