Week 11: POL 242 Review-Testing Hypotheses
Testing Hypotheses Alternative Approach
Take a point estimate and then taking the point estimate into a standardized value.
Keep your probabilbility value and take a point estimate into a standized value (for
Compare prob. Values rather than T scores. (it makes more sense)
1.96 makes more sense than 0.04.
Example: Suppose that you want to test the hypothesis that the proportion of the public who
favors some public policy is not equal to 50%. You draw a sample of 1200 Canadians and find
that 53.2% are in favor. Please test this hypothesis using the alternative approach.
Solution: Compare TS and CV. Our P value is less than our alpha value. We reject our null
hypothesis. Our substantive conclusion…
We use our line of best fit to estimate the type of relationship.
Generally, we use the line that minimized the sume of the square of the residuals
What is a residual? Minimize the distance from the points to the line of best fit.
Regression and Strength
We try to estimate the size of slope coefficient. Essentially, the slop coefficient, how
much change is made to DV when change is made to IV.
Our estimated slope is very small, T value is small and P value is really large.
Regression and inference
Can we generalize any possible relationship that we can find between IV and DV?
Can we infer that there is a true relationship between the population?
When T scores are small, (we are comparing T score and to standized value of 1.96 and -
1.96) . The T score is 8 which is far beyond the standized value of 1.96.
Therefore we reject hypothesized value.
Compare P value to alpha.
All we have to do is TO ALWAYS COMPARE P VALUE TO OUR ALPHA VALUE. This is much
easier to determine whether to reject or accept our null hypothesis. The number -8.202
is much bigger than -0.5.
Regression and goodness of fiti (besides looking at strength of relationship, we often have to
look at the overall goodness of fit)
Estimated line of best fit.
How accurate is our explanation of the dependent