Statistics Review

Question 1

Descriptive statistics

Answer 1

A set of statistics used to organize and summarize the properties of a set of data.

Question 2

Data matrix

Answer 2

A grid presenting collected data.

Question 3

Frequency distribution

Answer 3

A table that gives a visual picture of the observations on a particular variable.

Question 4

Frequency histogram

Answer 4

A data visualization technique showing how many of the cases in a batch of data scored each possible value, or range of values, on the variable.

Question 5

Dot plot

Answer 5

A data visualization technique in which every data point for a given variable is represented.

Question 6

Central tendency

Answer 6

A measure of what value the individual scores tend to center on.

Question 7

Mode

Answer 7

The value of the most common score; the score that was received by more members of the group than any other.

Question 8

Bimodal

Answer 8

A distribution with more than one mode or score.

Question 9

Multimodal

Answer 9

Having more than two modes or scores.

Question 10

Median

Answer 10

The value at the middlemost score of a distribution of scores; the score that divides a frequency distribution into halves.
Most appropriate measure of central tendency when a set of data has outliers.

Question 11

Mean

Answer 11

An arithmetic average; a measure of central tendency computed from the sum of all scores in a set of data, divided by the total number of scores.
Most common measure of central tendency (but not always the most appropriate way to measure central tendency).

Question 12

Variance

Answer 12

A computation that quantifies how spread out the scores of a sample are around their mean; it is the square of the standard deviation.

Question 13

Standard deviation

Answer 13

A computation that measures how far, on average, each score is in a data set from the mean.

Question 14

Box plot

Answer 14

A data visualization technique that depicts a sample’s median, interquartile range (25th and 75th percentiles), and outliers. AKA box and whiskers plot.

Question 15

Outlier

Answer 15

A score that stands out as either much higher or much lower than most of the other scores in a sample.

Question 16

z score

Answer 16

A computation that describes how far an individual score is above or below the mean, in standard deviation units. Also called standardized score.

Question 17

Z score formula

Answer 17

z = (X - M)/SD

Question 18

Variance formula (compute sample SD)

Answer 18

SD^2 = Σ(X - M)^2/N

Question 19

Standard deviation formula

Answer 19

SD = square root of (SD)^2

Question 20

Variance formula (estimate pop SD)

Answer 20

SD^2 = Σ(X - M)^2/(N - 1)

Question 21

r formula

Answer 21

r = Σ(z[x]z[y])/(N - 1)

Question 22

Cohen’s d

Answer 22

A measure of effect size indicating how far apart two group means are, in standard deviation units. Tells us how much overlap there is between the two sets of scores.

Question 23

Cohen’s d formula

Answer 23

d = (M1 - M2)/SD[pooled]

Question 24

Inferential statistics

Answer 24

A set of techniques that use data from a sample to estimate what is happening in the population.

Question 25

Point estimate

Answer 25

A single estimate based on our sample data of the true value in the population. May be a percentage, difference between means, or relationship between two variables.

Question 26

Confidence interval (CI)

Answer 26

A given range indicated by a lower and upper value that is designed to capture the population value for some point estimate (e.g., percentage, difference, or correlation); a high proportion of CIs will capture the true population value.

Question 27

Population Estimation

Answer 27

1. Our research question is about the whole population.
2. The quality of the sample data matters.
3. The population value is unknown.
4. Larger samples give more certain estimates.
5. To get a better estimate, we should do more than one poll.

Question 28

The steps of estimation and precision

Answer 28

1. State a research question using terms such as “how much” or “to what extent”.
2. Design a study: operationalize the question in terms of variables that are either manipulated or measured.
3. Collect the data and compute the point estimate and confidence interval.
4. Interpret the results in the context of your research question.
5. If possible, conduct the study again and meta-analyze the results.

Question 29

Margin of error of a percent estimate formula

Answer 29

SD * sqare root of (1/N) * 1.96

Question 30

Standard error

Answer 30

The typical, or average, error researchers make when estimating a population value.

Question 31

The constant associated with 95% confidence

Answer 31

1.96. This is a Z score from a normal distribution.

Question 32

Dependent samples design

Answer 32

A design in which each person has two scores because they were tested under two conditions, and we are interested in the difference between them. Also called a paired design.

Question 33

Null hypothesis significance testing (NHST)

Answer 33

An inferential statistical technique in which a result is compared to a hypothetical solution in which there is no relationship or no difference.

Question 34

p value

Answer 34

In NHST, the probability of getting the result in a sample or one more extreme, by chance, if there is no relationship or difference in the population.

Question 35

The NHST procedure

Answer 35

1. Assume that there is no effect (null hypothesis)
2. Collect data and calculate your result
3. Calculate the probability of getting a result of that magnitude, or one even more extreme, if the null hypothesis is true
4. Decide whether to reject or retain the null hypothesis

Question 36

Statistically significant

Answer 36

In NHST, the conclusion assigned when p < .05; that is, when it is unlikely the result came from the null-hypothesis population. There is a less than 5% chance that the null hypothesis is true.

Question 37

Not statistically significant

Answer 37

In NHST< the conclusion assigned when p > .05; that is, when it is likely the result came from the null-hypothesis population.

Question 38

Alpha level

Answer 38

The value, determined in advance, at which researchers decide whether the p value obtained from a sample statistic is low enough to reject the null hypothesis or too high, and thus retain the null hypothesis.