Basic Stats & Hypothesis Testing

Question 1

Inferential Stats

Answer 1

Used to interpret or draw inferences about a set of observations (e.g. t-test, ANOVA)
Making inferences about a population from a sample of observations

Question 2

Descriptive Stats

Answer 2

Used to summarize or describe a set of observations (e.g. mean, variance)

Question 3

Mean

Answer 3

Average score calculated by the sum of the scores divided by the total number of scores
Sensitive to exact value of all scores in distribution

Question 4

Median

Answer 4

Midmost score in the series of n scores; must arrange them hierarchically before finding this
Less sensitive to outliers

Question 5

Mode

Answer 5

Score that occurs with greatest frequency
Can have more than one mode

Question 6

Measures of Central Tendency

Answer 6

Mode
Median
Mean

Question 7

Measures of Variability

Answer 7

Range
Variance
Standard Deviation
Deviation Score

Question 8

Range

Answer 8

Distance between the highest and lowest score
Measure of dispersion
Only gives info about the 2 extreme scores

Question 9

Variance

Answer 9

Average of [how much each score deviates from the mean]^2

Question 10

Standard Deviation

Answer 10

Square root of the variance

Question 11

Deviation Score

Answer 11

Tells us how far away the raw score is from the mean of the distribution
Calculate the mean, subtract each score from the mean
Sum of deviations scores = 0, so we square each deviation score before summing

Question 12

Shapes of Distributions

Answer 12

Rectangular distribution
Bimodal
Normal (bell curve)
Positively/negatively skewed

Question 13

Statistic

Answer 13

Computed on a sample
Sample mean (X) is an estimate of the population mean (𝜇)
Sample variance (S2) is an estimate of the population variances (𝛔2)
The sample standard deviation (S) is an estimate of the population standard distribution (𝛔)

Question 14

Parameter

Answer 14

Computed on a population

Question 15

Random Sampling (selection)

Answer 15

Choose random subset of the population ensuring that each member of the population has an equivalent chance of being sampled
Examine that sample and use your observations to draw inferences about the population
If a sample is representative is has external validity - important for generalizing the results to the population

Question 16

Random Assignment

Answer 16

Whereas random sampling refers to the source of the data (and is important for external validity), random assignment refers to how subjects are assigned to groups
Important for integrity of the experiment (internal validity)
Random assignment reduces the likelihood that groups differ in some critical way other than the treatment

Question 17

Means Have Distributions

Answer 17

If you repeatedly draw samples, compute means, the means will form a distribution
As N increases, the distribution becomes more normal
This allows us to draw inferences about populations from samples drawn from those populations

Question 18

Standard Error of the Mean

Answer 18

Variability between means if took repeated samples from the population
How much variability you’d expect to find if you took repeated samples from the same population
SD / sqrt[n] - denominator of t test
Standard error of mean allows inferences of populations
Larger n = smaller SEM
Larger SD = larger SEM

Question 19

Dependent T-Test

Answer 19

Also called paired t-test, matched samples, repeated measures
The same participants give us data on two measures (e.g. before and after treatment)
More powerful
All calculations on difference scores
Value always higher than independent

Question 20

Dependent T-Test Pros

Answer 20

Eliminate subject to subject variability
Control for extraneous variables
Need fewer subjects

Question 21

Dependent T-Test Cons

Answer 21

Order/carry-over effects
Subjects no longer naive
Sometimes not logically possible

Question 22

Variance sum law

Answer 22

Variance of the differences between two independent variables is equal to the sum of their variances