Week 8

Question 1

Descriptive Statistics

Answer 1

Statistics that describe the results as they are - “get to know” the data.

Question 2

Central Tendency

Answer 2

Different ways of measuring the average.
Mean (average),
Median (middle),
Mode (most frequent).

Question 3

Variability

Answer 3

Range (difference between highest and lowest value). 
Standard Deviation (the average distance between the scores and the mean).

Question 4

Effect Size

Answer 4

A measure of the strength/magnitude of a statistical relation.

Question 5

Interpretation Guidelines (d)

Answer 5

Small: 0.2
Medium: 0.5
Strong: 0.8.

Question 6

Interpretation Guidelines (r)

Answer 6

Small: .1
Medium: .3
Strong: .5.

Question 7

Cohen’s d

Answer 7

The difference between two means in standard deviation units. Make comparisons between different studies by standardizing.

Question 8

Inferential Statistics

Answer 8

Making educated guesses based on acceptable mistakes.

Question 9

Null Hypothesis

Answer 9

Hypothesis that population means are equal.
Relationship in sample is sampling error.
No relationship in the population.

Question 10

Research Hypothesis

Answer 10

Hypothesis that population means are different.
There is a relationship in the population.
Relationship in the sample reflects the relationship in the population.

Question 11

P-Value

Answer 11

Probability that the data would happen if the null were true.

Question 12

Type I Error

Answer 12

False positive. Saying there is an effect in the population when there is not. Rejecting null when null is true. Alpha.

Question 13

Type II Error

Answer 13

False negative. Saying there is not an effect in the population when there really is. Fail to reject null when null is false. Beta.

Question 14

Statistical Power

Answer 14

The probability of rejecting the null given the sample size and expected relationship strength.
The complement of the probability of committing a type II error.

Question 15

Four Criticisms of NHST

Answer 15

1. Most people don’t understand p-values.
2. Oversimplifies as “significant/not-significant”: incentivizes cheating/questionable practice.
3. Pointless - the null is almost never true.
4. We’re actually interested in effect sizes.

Question 16

Distributions

Answer 16

The way the scores are distributed across levels of that variable.

Question 17

Negatively Skewed

Answer 17

Peak to upper range - skewed to the left.

Question 18

Positively Skewed

Answer 18

Peak to lower range - skewed to the right.

Question 19

Restriction of Range

Answer 19

When one or both variables have a limited range in the sample relative to the population.
Ex: age as a variable, but only having access to 18-25 year olds.

Question 20

Parameters

Answer 20

Corresponding values in the population

Question 21

Sampling Error

Answer 21

Random variability in a statistic from sample to sample.

Question 22

Alpha Level

Answer 22

How low the p-value must be for the sample result to be considered unlikely.
Rejecting null when null is true.

Question 23

Practical Significance

Answer 23

The importance or usefulness in a real-world context.

Question 24

Correlation Matrix

Answer 24

Presenting correlations (r) among several variables.

Question 25

File Drawer Problem

Answer 25

Type I, researchers publishing statistically significant results, and filing non. Published probably contains a high amount of type I error.

Question 26

Nonlinear

Answer 26

Those in which the points are better fit by a curved line.

Question 27

Negative linear

Answer 27

Higher scores on one variable are associated with low scores on the other.

Question 28

Positive Linear

Answer 28

Higher scores on one variable are associated with high scores on the other.