[PSYC 300] - Midterm Exam Guide - Ultimate 21 pages long Study Guide!

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PSYC 300
MIDTERM EXAM
STUDY GUIDE
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Exam 3 Study Guide PSYC300: Chapters 8-14
CHAPTER 8: Hypothesis Testing and Inferential Statistics
1. Understand what inferential statistics are and how they are used to test a research
hypothesis.
Inferential statistics estimate the probability that our results were due to chance.
It helps infer how the results from your sample might generalize to the
population, giving the ability to draw conclusions about your data (sample →
population). When P is small, we are “happy.”
When P is small, it means there’s a significant difference between the two
groups that you're testing (can reject the null)
2. Define the null hypothesis.
The null hypothesis describes that there is no difference between groups; the
independent variable had no effect on the dependent variable. Nothing
happened!
When P is large, we “fail to reject the null hypothesis”
Alternative: IV had an effect on DV so we “reject the null”
3. Define alpha.
The standard that the observed data must meet
normally set at .05, which means that we may reject the null hypothesis only if
the observed data is so unusual that they would have occurred by chance at
most 5% of the time
The smaller the alpha, the more stringent the standard is
alpha level: probability of making type I error
4. Understand what the p-value is and how it is used to determine statistical
significance.
The p-value is the probability value of a statistic that shows the likelihood of an
observed statistic occurring on the basis of the sampling distribution. (indicates how
extreme the data is)
if P < alpha, then we reject the null hypothesis, and we say that the result is
statistically significant
if P > alpha, then we fail to reject the null hypothesis, and we say that the result is
statistically nonsignificant
statistical significance= (effect size)(sample size)
Decision errors
setting strict significance level like p<.001
decreases type I error
increases type II error
setting lenient significance level like p<.10
increases type I error
decreases type II error
5. Understand why two-sided p-values are used in most hypothesis tests.
Two-sided p-values are used more often because the data need to be interpreted, even
if they are in an unexpected direction, to test their research hypothesis.
always twice as big as the one-sided p-value
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provides a more conservative statistical test and allow us to interpret statistically
significant relationships even if those differences are not in the direction
predicted by the research hypothesis
6. Define Type 1 and Type 2 errors and understand the relationship between them.
Type 1 error occurs when we reject the null hypothesis when it is in fact true
The probability of a researcher making a Type 1 error is equal to alpha. When a
= .05, we know we will make a Type 1 error no more than 5% (5/100) of the time,
and when a = .01, we know we will make a Type 1 error not more than 1%
(1/100) of the time
Control Type 1 errors by making the alpha level as small as possible
“False alarm”, and the worst error
Type 2 error refers to the mistake of failing to reject the null hypothesis, when the null
hypothesis is really false
Beta level
Power = 1-B
Type 2 errors are more common when the power of a statistical test is low
B=.20 or less and power=.80 is good
“Miss”
7. Understand beta and how it is related to the power of a statistical test.
Beta is the probability of a researcher making Type 2 error. The power of a statistical
test is the probability that the researcher will, on the basis of the observed data, be able
to reject the null hypothesis, given that the null hypothesis is actually false and thus
should be rejected. Power = 1 - Beta
statistical power: probability that a study will produce a statistically significant
result if the research hypothesis is true
can be determined from power tables
depends primarily on effect/sample size
more power if..
bigger difference between means (use more intense
experimental procedure)
smaller population standard deviation
more people in study
also affected by type of hypothesis test and 1 v 2 tailed test
2 distributions may have little overlap but high power because the 2
means are very different or the variance is very small
power interpretation with results
significant
sample small, effect significant
sample large, effect too small
insignificant
sample small, study inconclusive
sample large, hypothesis probably false
8. Understand the effect size statistic and how it is used.
The effect size is indicated by the size of a relationship; it indicates the magnitude of a
relationship: zero indicates that there is no relationship between the variables, and
larger (positive) effect sizes indicate stronger relationships.
small= 0.2
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