Interpreting P and q values in the results of genomic data analysis

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This video has been made a primer to the use of P and q values that are used to indicate the significance of results in genomic data analysis and elsewhere.

It is based upon an article presented by the company Nonlinear on their web site:

[EDIT May 2015]
The above URL now redirects to:

If you have any comments please let me know.

[TheAbdirahman]

Correction
Slide titled: Multiple test P-value adjustment methods
Bonferroni correction method is P-value divided by number of tests performed (NOT P-value X number of tests as presenter claimed).
Example - Microarray gene expression data with 20, 000 genes test.
Bonferroni correction is 0.05/20, 000. Hence, adjusted P-value would be

[carolinedahl]

My understanding is that more tests give rise to a higher rate of false positives; i.e. the % risk increases with test number. So if two populations have the same mean and you do 100 tests, at p=0.05 you will actually have more than 5 tests that suggest that the means are different. Bonferroni corrects the p at which you accept a significant difference of means, given an input p and the number of tests. So assuming that Bonferroni is correct, microarrays have many more false positives than the t-test suggests.

[neerajbudhlakoti]

are you sure about biological and technical replicate. please check it out ????

[RayofAmity]

does increasing the sample size affect the FDR threshold??