Prep for Psyc 201 Exam 2

Question 1

Highly likely to chance

Answer 1

High p-value

Question 2

Less likely to chance

Answer 2

Low p-value

Question 3

Probability of the data given the null hypothesis- NOT the probability that a particular state of the world is true and NOT the probability that the null hypothesis is false

Answer 3

p-value

Question 4

A statistical method that uses sample data to evaluate a hypothesis about a population. The general goal is to rule out chance (sampling error) as a plausible explanation for the results from a research study.

Answer 4

Hypothesis Test (Two tailed test)

Question 5

The hypothesis that there is no significant difference between specified populations. Predicts there is NO relationship

Answer 5

Null hypothesis

Question 6

A statement that directly contradicts the null hypothesis. Predicts there is a relationship.

Answer 6

Alternate hypothesis

Question 7

the probability value that is used to define the vary unlikely sample outcomes if the null hypothesis is true

Answer 7

significance value (alpha level)

Question 8

Extreme sample values defined by alpha level, used to reject/retain H0

Answer 8

Critical region

Question 9

The statistical hypotheses specify either an increase or decrease in the population score

Answer 9

Directional hypothesis test (one-tailed test)

Question 10

When the probability of certain results are beyond a critical region if the null hypothesis is true. It is determined by the p-value.

Answer 10

Statistical significance

Question 11

Concerned with whether the results are used in the real world. It is determined by effect size.

Answer 11

Practical significance

Question 12

Gives us an idea of how large, important, or meaningful a statistically significant effect is.

Answer 12

Effect size

Question 13

Sample mean larger than the hypothesized mean

Answer 13

Positive value of Cohen’s d

Question 14

Sample mean smaller than the hypothesized mean

Answer 14

Negative value of Cohen’s d

Question 15

rejecting the H0 when it is actually true. Affected by the alpha level. Alpha level is the probability of rejecting the H0 when it is actually true. Smaller alpha level, smaller risk of false positive

Answer 15

Type I Error (false positive)

Question 16

Failing to reject H0 when it is actually false and H1 is true. Affected by the power of the study

Answer 16

Type II Error (false negative)

Question 17

Defined by the probability that the test will reject the null hypothesis when the treatment does have an effect.

Answer 17

The power of hypothesis testing

Question 18

Larger sample size, increased power to detect an effect

Answer 18

Sample size affecting power

Question 19

Larger alpha level, less restriction in detecting an effect

Answer 19

alpha level affecting power

Question 20

use of statistics in which hypotheses are based on or informed by one’s results.

Answer 20

Hypothesizing After the Results are Known (HARK)

Question 21

An exploitation of data analysis in order to discover patterns which would be presented as statistically significant, when there is no underlying effect.

Answer 21

p-hacking

Question 22

Probabilities are calculated based on a sampling distribution. Doesn’t require knowing the population standard deviation; instead, we use an estimate of s. The estimate becomes more accurate with larger sample sizes. Different curves = different critical values. Higher degrees of freedom = more similar to the normal curve

Answer 22

t Distribution

Question 23

Comparing one sample mean to a predicted population value.

Answer 23

One-sample t-test

Question 24

Calculated with the estimated standard error

Answer 24

t-value

Question 25

The margin of error around our point estimate. Can only be calculated for two-tailed tests.

Answer 25

Confidence interval

Question 26

Less error, more precise

Answer 26

Narrow CI’s

Question 27

More error, less precise

Answer 27

Large CI’s

Question 28

X-bar +/- t*(s/sq root of n)

Answer 28

Confidence Interval Formula (Two-tailed test)

Question 29

An experimental design in which subjects are exposed to all levels of the independent variable and their score son the dependent variable are compared. A single sample of individuals is measured more than once on the same dependent variable.

Answer 29

Repeated measures

Question 30

Comparing the mean difference between two measurements from one sample.

Answer 30

Paired samples t test

Question 31

A response to some manipulation or natural occurrence

Answer 31

Changes and Differences

Question 32

Individuals that are compared within groups, not between groups. Ex: Spouses, siblings, twins

Answer 32

Matched or paired individuals

Question 33

compares the sample means of two independent groups in order to determine whether the associated population means are significantly different among a single variable

Answer 33

Independent measures

Question 34

Comparing two sample means between independent groups from one point in time

Answer 34

Independent samples t test

Question 35

Difference between group means +/- margin of error

Answer 35

Confidence Interval Formula (Independent Samples t test)

Question 36

The combined estimate of variance using the information from each sample

Answer 36

Pooled variance estimate

Question 37

t = (Sample mean G1 - Sample mean G2)/ estimated standard error

Answer 37

Independent samples t test formula

Question 38

t = (mean difference - hypothesized mean difference)/ estimated standard error of mean differences

Answer 38

Paired samples t test formula

Question 39

t = (sample mean - population mean)/estimated standard error

Answer 39

One sample t test formula

Question 40

Standard pooled variance = (SS1+SS2)/(df1+df2)

Answer 40

Pooled variance formula

Question 40

SE = sq rt of (pooled variance/n1 + pooled variance/n2)

Answer 40

Estimated Standard Error for Independent-samples t test