1. The Two Sample t Review significance testing Review t distribution
Introduce 2 Sample t test / SPSS

2. Significance Testing State a Null Hypothesis
Calculate the odds of obtaining your sample finding if the null hypothesis is correct
Compare this to the odds that you set ahead of time (e.g., alpha)
If odds are less than alpha, reject the null in favor of the research hypothesis
The sample finding would be so rare if the null is true that it makes more sense to reject the null hypothesis

3. Significance the old fashioned way
Find the “critical value” of the test statistic for your sample outcome
Z tests always have the same critical values for given alpha values (e.g., .05 alpha  +/- 1.96)
Use if N >100
t values change with sample size
Use if N < 100
As N reaches 100, t and z values become almost identical
Compare the critical value with the obtained value  Are the odds of this sample outcome less than 5% (or 1% if alpha = .01)?

4. Critical Values/Region for the z test ( = .05)

5. Directionality Research hypothesis must be directional
Predict how the IV will relate to the DV
Males are more likely than females to…
Southern states should have lower scores…

6. “2-Sample” t test Apply when… Components Null Hypothesis (Ho)
You have a hypothesis that the means (or proportions) of a variable differ between 2 populations
Components
2 representative samples – Don’t get confused here (usually both come from same “sample”)
One interval/ratio dependent variable
Examples
Do male and female differ in their aggression (# aggressive acts in past week)?
Is there a difference between MN & WI in the proportion who eat cheese every day?
Null Hypothesis (Ho)
The 2 pops. are not different in terms of the dependent variable

7. 2-SAMPLE HYPOTHESIS TESTING
Assumptions:
Random (probability) sampling
Groups are independent
Homogeneity of variance
the amount of variability in the D.V. is about equal in each of the 2 groups
The sampling distribution of the difference between means is normal in shape

8. 2-SAMPLE HYPOTHESIS TESTING
We rarely know population S.D.s
Therefore, for 2-sample t-testing, we must use 2 sample S.D.s, corrected for bias:
“Pooled Estimate”
Focus on the t statistic:
t (obtained) = (X – X)
σ x-x
we’re finding the
difference between the two means…
…and standardizing this difference with the pooled estimate of the standard error

9. 2-SAMPLE HYPOTHESIS TESTING
2-Sample Sampling Distribution
– difference between sample means (closer sample means will have differences closer to 0)
t-test for the difference between 2 sample means:
Does our observed difference between the sample means reflects a real difference in the population means or is due to sampling error?
- t critical t critical
ASSUMING THE NULL IS TRUE!

10. Applying the 2-Sample t Formula
Example:
Research Hypothesis (H1):
Soc. majors at UMD drink more beers per month than non-soc. majors
Random sample of 205 students:
Soc majors: N = 100, mean=16, s=2.0
Non soc. majors: N = 105, mean=15, s=2.5
Alpha = .01
Degrees of Freedom = N-2
What is the null? Can it be rejected?
FORMULA:
t(obtained) = X1 – X2
pooled estimate of standard error

11. Example 2
Dr. Phil believes that inmates with tattoos will get in more fights than inmates without tattoos.
Tattooed inmates  N = 25, s = 1.06, mean = 1.00
Non-Tattooed inmates  N = 37, s =.5599, mean =
Null hypothesis?
Directional or non?
tcritical?
Difference between means?
Significant at the .01 level?

12. 2-Sample Hypothesis Testing in SPSS
Independent Samples t Test Output:
Testing the Ho that there is no difference in number of adult arrests between a sample of individuals who were abused/neglected as children and a matched control group.

13. Interpreting SPSS Output
Difference in mean # of adult arrests between those who were abused as children & control group

14. Interpreting SPSS Output
t statistic, with degrees of freedom

15. Interpreting SPSS Output
“Sig. (2 tailed)”
gives the actual probability of obtaining this finding if the null is correct
a.k.a. the “p value” – p = probability
The odds are NOT ZERO (if you get .ooo, interpret as <.001)

16. “Sig.” & Probability
Number under “Sig.” column is the exact probability of obtaining that t-value (finding that mean difference) if the null is true
When probability > alpha, we do NOT reject H0
When probability < alpha, we DO reject H0
As the test statistics (here, “t”) increase, they indicate larger differences between our obtained finding and what is expected under null
Therefore, as the test statistic increases, the probability associated with it decreases

17. Example 2: Education & Age at which First Child is Born
H0: There is no relationship between whether an individual has a
college degree and his or her age when their first child is born.
Okay, let’s go through an example.
Here we’re looking at the same data set.
Except this time, we’re testing this null hypothesis…
There you see the mean age of these college men and women. Women are slightly older. Is this difference statistically significant?

18. Education & Age at which First Child is Born
What is the mean difference in age?
What is the probability that this t statistic is due to sampling error?
Do we reject H0 at the alpha = .05 level?
Do we reject H0 at the alpha = .01 level?

19. SPSS In-Class Conduct an independent sample t-test
Need one I/R variable
This is the variable used to calculate means
Need on Nominal, 2-category (dummy) variable
This dictates the “groups” used to create the two different means