lecture 4
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Statistical hypothesis
is a testable statement with a probability distribution
One sample test (T-Test)
Compares the mean of a single sample to a predetermined value.
Null hypothesis for One sample Test (T-test)
the sample mean is = to the specified value. the null hypothesis would be that there is no/null/zero difference
Alternative hypothesis for one sample Test(t -test)
the sample mean is not = to the specified value. the alt. would be that the sample mean is different from the specific value
Two sample test (t-test)
Compares two independent sample means against each each other. (by testing the probability that the difference in the means =0
the null hypothesis (two sample t-test)
null hypo: is that both means = 0 which mean that the (mean of sample one - mean of sample 2= 0
the alternative hypothesis ( two sample t-test)
alt hypothesis: that the means of the samples are different from each other aka doesn’t = 0 after subtracting
Test statistic
is where on the probability distribution the sample lies
T-score
represents how many standard deviations a data point is away from the mean in a t-distribution Note used: in t-tests.
P-value
is the probability of getting a test statistic of that size or greater
Type I error
where you reject the null when it is true (putting an innocent in prison)
type II error
when you accept the null but it is false. (letting the guilty go free)
scientific hypothesis
A statement that is testable with observations
when P<.05
reject the null hypothesis (is statistically significant
when P> .05
accept the null (is not statistically significant