Hope you are all doing well...
Can anybody explain the main difference between f-test and t-test?,
where i want to use it as method to identify the effective features among a list 60 features extracted out of two types of data (i.e, data 1 composed of 30 image for each image 60 features are extracted/ data 2 composed of 40 images for each image 60 features are extracted), i do search but i still misunderstand the difference..
Note that: the matlab equations for t- test is (h = ttest2(x,y)) and f-test is (H = vartest2(X,Y))
t-test is used to test if two sample have the same mean. The assumptions are that they are samples from normal distribution.
f-test is used to test if two sample have the same variance. Same assumptions hold.
I have little to no experience in image processing to comment on if these tests make sense to your application. A little more info of the problem you are trying to solve will be useful.
If you are however solving a classification problem (categorizing your images) You can use stepwise logistic regression with F-statistics criterion to reduce your predictor dimension:
Alternatively you could use PCA as Image Analyst suggested which does not take into account the response when reducing the dimensionality.
Were the Wikipedia explanations not understandable? Anyway, I'm no statistician but I think you'd want Principal Components Analysis, rather than t-test of F-test, if you want to figure out which of 60 features are the most important.
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