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

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PSYC 300

MIDTERM EXAM

STUDY GUIDE

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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