Independent Groups
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What is a completely randomised design?
 A true experiment
 P's are placed randomly into only 1 condition
 This reduces bias as individual differences are distributed nonsystematically across conditions

What are quasi experiments?
 Naturally occurring DV
 Logically impossible to assign randomly
 Should not impute causality as the design is correlational and there could variables that change systematically with the DV (confounding variable)

Which experiments require an independent T test?
Either true or Quasi

What assumptions are made when using an independent T?
 Normal data
 Homogeneity of variance

What are the null hypotheses for independent T?
 H0=u1=u2
 H0=u1u2=0 (for when there are 2 experimental groups)
 This shows that the results are from the same population of scores

What are the alternate hypotheses for a 2 sided independent T?
HA=u1≠u2 or HA=u1u2≠0

What are the alternate hypotheses for a 1 sided independent T?
HA=u1>u2

What do we have to consider for the independent T and why?
 The sampling distribution of the difference between the 2 means
 The scores are not in pairs

What does the variance sum law state?
The variance of a sum or difference between two independent random variables is equal to the sum of their respective variances

What is the standard error equal to for the independent T?
The standard deviation of the set divided by the square root of the number of data points in the set

What is the variance equal to for the independent T?
Standard deviation squared over the number of data points

When can we compute z scores?
When the SD is known

What do we do when we cannot find the SD?
 Estimate it using the two sample standard deviations
 Average information from both samples to provide a pooled estimate of the parameter value

How do we obtain a T value without an SD?
We substitute the estimate for the parameter value with the actual one

How do we estimate the parameter value for the T test?
 Take a weighted average of the two sample standard deviations
 This is because of the homogeneity of variance assumption

Why do we use a weighted average?
 To give a more accurate estimate
 If the sample sizes are different the average gives more weight to the larger one

How do we find this weighted average?
 Add the sum of squares
 Add (n11) + (n21)
 Square root the whole thing