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T tests and f test

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t-tests and F-test STATISTICS IS CONCERNED WITH TRYING TO DRAW CONCLUSIONS ABOUT VERY LARGE GROUPS OF INDIVIDUALS (ANIMATE OR INANIMATE) WHEN WE CAN STUDY SMALL SAMPLES OF THEM.  Both t-tests and F-tests are used in situations where we want to compare just two populations.  We can use a t-test to answer the question:  How confident can I be that the two populations have different means?  Or an F-test to answer the question  How confident can I be that the two populations have different variances?  Remember that statistical test actually test how likely the null hypothesis is to be true.  A major attraction of t-test and F-test is that they produce straightforward and easy to interpret results. Limitations and assumptions The following assumptions or requirements apply to t-test and F-test:  Random sampling  Independent measurement or observation  Normal distributions  Equal variance (t-test only)  Random sampling  The individuals or individual points in a sample should be selected by some random process in such a way that every individual or point in the population has an equal chance of being selected.  Independent measurements or observations   It is assumed that each individual in the sample would behave in the same way, regardless of how other members of the sample behave. Statistical test assume that no member of a sample is influenced in its behavior by the others.  Normal distribution   The ...
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