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Further Hypothesis Testing

sophie waddington 3 questions

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  1. 1Assumptions of T tests
    > we assume that observations are [blank_start]independent[blank_end] > the observations are in the 2 groups are unrelated
    > [blank_start]normal[blank_end] distribution for each sample
    > equal [blank_start]variances[blank_end] in the two samples

    if the variances prove not to be equal you can perform a t test with unequal variances > this carries out a more strict t test with [blank_start]decreased[blank_end] degrees of [blank_start]freedom[blank_end]
    1. Aindependent
    2. Bnormal
    3. Cvariances
    4. Ddecreased
    5. Efreedom
  2. 2if normal distribution and/or equal variance of the data is [blank_start]not[blank_end] met we can [blank_start]transform[blank_end] the data

    transformation
    - e.g. takings logs or [blank_start]square[blank_end] roots > brings in the extreme values that might be effecting the [blank_start]normal[blank_end] distribution

    after transformation we can test the means of the transformed data in a t-test
    1. Anot
    2. Btransform
    3. Csquare
    4. Dnormal
  3. 32 dependent samples
    - sometimes we have situations were every measurement in one group is related to a particular measurement in the other group (before and after treatments)

    the data is in '[blank_start]matched[blank_end] pairs' form

    so instead of doing 2 sample t test (independent samples) we do a [blank_start]paired[blank_end] t test

    ASSUMPTIONS OF PAIRED T TESTS
    - the paired t test assumes the [blank_start]differences[blank_end] (e.g. before and after) are approximately [blank_start]normally[blank_end] distributed > it makes no assumptions about the before and after samples separately

    in an example with 6 data points, this does not give us much chance to prove a difference between a variable > the study is [blank_start]not[blank_end] very powerful due to its small sample size

    [blank_start]bigger[blank_end] (er) sample size = more power!

    stat > power and sample size > 2 sample t
    - leave sample size empty because we want minitab to tell us what we need
    - if we wanted to find a difference of 4 between means, put that in box
    - power value = 0.8 (8%)
    - standard deviation = whatever it is

    sample size = if wanted to find that difference/significant result (if there is one) 80% of the time then we need a [blank_start]sample[blank_end] size of at least..... answer
    1. Amatched
    2. Bpaired
    3. Cdifferences
    4. Dnormally
    5. Enot
    6. Fbigger
    7. Gsample

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Description
Stats Quiz on Further Hypothesis Testing, created by sophie waddington on 28/12/2018.
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