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8 Cards in this Set

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  • Back

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 1-incorectly rejecting the true null hypothesis (alpha)

Type 2-failing to reject a false null hypothesis


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