CH 13 NEW RM Inferential Stats

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Chapt. 13, research methods "Understanding research results: statistical inference"

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


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

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