How can we help?

You can also find more resources in our Help Center.

16 terms

CH 13 NEW RM Inferential Stats

Chapt. 13, research methods "Understanding research results: statistical inference"
STUDY
PLAY
statistical significance
the difference between 2 (or more) means [results] is not likely to have occurred by chance. IV had effect on DV; difference not due to measurement or sampling error
value for statistical significance
0.05. [any difference that is so large that it would occur by chance fewer than 5 times out of 100 (.05) is statistically significant]
t-tests vs. F tests
t-tests are used to determine whether 2 scores differ by chance. F-tests are used to determine if 3 or more scores differ by chance
what are F and t-tests ratios of?
a ratio of the variability between groups to the variability within each group [b/t grps:w/in each grp]
How do you interpret the results of an F/t-test?
If the between groups variability is much larger than within groups variability, we would conclude our IV had an effect.
What is the probability of making Type I error?
Equal to our alpha level, which is typically .05.
Type I Error
Say there is an effect when there actually isn't Rejection of null hypothesis but null hypothesis is actually TRUE (we should not have rejected it).
Type II Error
say there isn't an effect when there actually is Failure to reject the null hypothesis when we actually should have. type II= 1-Power
Why are nonsignificant results hard to interpret?
There may have been no effect of our IV OR there could have been other reasons: manipulation may have failed, sample may have been biased, may have been a confounding variable, or effect of IV may have been so weak that we didn't have the POWER to detect it.
null hypothesis
the population means are equal. samples came from the same population- the observed difference is due to random error.
research hypothesis
population means are NOT equal. samples came from different populations; same population or no effect on the IV (no difference between the b/t and within groups)
when is there possibility of a type I or a type II error?
Type I: when you say there is a relationship/effect; Type II: when you say there isn't a relationship/effect
Power
ability to detect a REAL effect (the ability to detect a difference)
Things effecting power
1) Sample size (^=^ power) 2) strength of manipulation (and good operational defs) (maximize the between grps. variability); 3) reduce within groups variability
Effect size
r-squared; what's the practical effect?; how meaningful a significant effect is; ex: if r= .6, then r2= .36 so 36% of "___" is accounted for by "_ _ _ _ _"
What is considered a small, medium, or large effect?
small: .10 - .20; medium: .20 - .39; large: .40 and up