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8 Cards in this Set
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Explain The Central Limit Theorem 
the average of a bunch of samples is more close to the population mean than any 1 sample assuming a sample distribution has a mean and st dev, the sampling distribution of the mean has a mean = population mean, and st dev = σ / √N 

According to CLT, what is the shape of the sampling distribution of sample means 
the sampling distribution of the mean will be normal if the population distribution is normal the sampling distribution of sample means will be near normal, even if the sample distribution is not normal 

What is important about the Normal Distribution? 
many data sets follow this distribution this distribution naturally occurs in nature it allows us to make assumptions about the population, (create predictions, tests etc.) 

Why is it useful to standardize data? 
allows us to speak about data in terms of st dev data is represented in terms of proportions helps us identify which scores are 'different' 

Define p<.05 
there is a 5% chance that a result occurred by chance if the null hypothesis (H0)is true denoted as alpha (type 1 error) 

Define type 1 and type 2 errors 
Type 1incorectly rejecting the true null hypothesis (alpha) Type 2failing to reject a false null hypothesis (beta) 

When do we use Student's T? 
when we don't know the population mean, substitute S for σ small sample size 

What factors affect the size of T 1) difference between the sample mean and population mean 2) size of S2 3) sample size 
1) as this difference increases, so does the likelihood of getting a significant result 2) as Sample variance decreases, the denominator decreases and T increases 3) as N increases, the denominator decreases and T increases 