1. Descriptive and inferential statistics

Question 1

Classify these variables as NOMINAL or CONTINUOUS:

A) Age

B) Gender

C) Height

Answer 1

A) Age = Continuous

B) Gender = Nominal

C) Height = Continuous

Question 2

Describe what a confounding variable is

Answer 2

A variable that affects the outcome being measured as well as, or instead of, the independent variable.

• because a confounding variable is an unforeseen and unaccounted-for variable that jeopardizes reliability and validity of an experiment’s outcome

Question 3

If a test is valid, what does this mean?

Answer 3

The test measures what it claims to measure

Question 4

If a test is reliable what does this mean?

Answer 4

The test will give consistent results.

Question 5

The discrepancy between the numbers used to represent something that we are trying to measure and the actual value of what we are measuring is called:

Answer 5

Measurement error

Question 6

What is the ‘fit’ of the model?

Answer 6

The ‘fit’ of the model is the degree to which a statistical model represents the data collected

Question 7

What is variance?

Answer 7

The variance is the average error between the mean and the observations made

Question 8

A frequency distribution in which low scores are most frequent (i.e. bars on the graph are highest on the left hand side) is said to be:

Answer 8

Positively skewed

Question 9

How can we compensate for practice effects?

Answer 9

Counterbalancing

Question 10

How can we compensate for boredom effects?

Answer 10

Giving participants a break between tasks

Question 11

Variation due to variables that have not been measured is known as:

Answer 11

Unsystematic variation

• Unsystematic variation results from random factors that exist between the experimental conditions (such as natural differences in ability, the time of day, etc.)

Question 12

What is the assumption of homogeneity of variance?

Answer 12

That the variance within each of the populations is equal.

Question 13

Variation due to the experimenter doing something in one condition but not in the other condition is known as:

Answer 13

Systematic variation

Question 14

What does residual variance tell us?

Answer 14

Residual variance helps us confirm how well a regression line that we constructed fits the actual data set. The smaller the variance, the more accurate the predictions are

Question 15

The purpose of a control condition is to

Answer 15

Allow inferences about cause

• A properly constructed control condition provides you with a reference point to determine what change (if any) occurred when a variable was modified

Question 16

What helps to control for participant characteristics (thus minimize unsystematic variation)?

Answer 16

Randomization

Question 17

How are Z scores calculated?

Answer 17

By subtracting the mean from the score and dividing the answer by the standard deviation

SCORE - MEAN = X

X / STDEV = Z-SCORE

Question 18

The standard deviation is the square root of the:

Answer 18

Variance

Question 19

What is the coefficient of determination?

Answer 19

A measure of the amount of variability in one variable that is shared by the other

Calculated as:

correlation coefficient squared

Question 20

Complete the following sentence:

A large standard deviation (relative to the value of the mean itself)…

Answer 20

Indicates that the data points are distant from the mean

(i.e. the mean is a poor fit of the data).

Question 21

The probability is p = 0.80 that a patient with a certain disease will be successfully treated with a new medical treatment. Suppose that the treatment is used on 40 patients. What is the “expected value” of the number of patients who are successfully treated?

Answer 21

32

because 80% of 40 patients is 32 (or 40 x .80 = 32)

Question 22

What is the Confusion of the inverse?

Answer 22

A logical fallacy whereupon a conditional probability is equated with its inverse

• that is, given two events A and B, the probability of A happening given that B has happened is assumed to be about the same as the probability of B given A, when there is actually no evidence for this assumption.

More formally, P(A|B) is assumed to be approximately equal to P(B|A).

Question 23

The test statistics we use to assess a linear model are usually _______ based on the normal distribution.

Answer 23

Parametric tests

Question 24

What are the assumptions of the general linear model?

Answer 24

Independence:

• The errors in your model should not be related to each other

Additivity/Linearity:

• If you have several predictors then their combined effect is best described by adding their effects together
• The outcome variable is, in reality, linearly related to any predictors

Normality:

• The core element of the
  Assumption of Normality asserts that the distribution of sample means (across independent
  samples) is normal.

(In technical terms, the Assumption of Normality claims that the sampling
distribution of the mean is normal or that the distribution of means across samples is normal)

Homogeneity of variance:

• When testing several groups of participants, samples should come from populations with the same variance

Question 25

Finish the sentence

The further the values of skewness and kurtosis are from zero, the more likely…

Answer 25

…it is that the data are not normally distributed

Question 26

Parameters are numbers that summarize data for…

Answer 26

an entire population

Question 27

Statistics are numbers that summarize data from…

Answer 27

a sample

Question 28

What are the measures of central tendency?

Answer 28

• Mean
• Median
• Mode

Question 29

What are the measures of spread or dispersion?

Answer 29

• Range
• Variance
• Standard Deviation

Question 30

What does kurtosis tell us?

Answer 30

what data points are outliers

Distributions:
Leptokurtic = relatively large tails (heavy drop off)

Platykurtic = relatively small tails (light/no drop off)

Mesokurtic = same kurtosis as the normal distribution

Question 31

What is Gambler’s fallacy?

Answer 31

mistaken belief that, if something happens more frequently than normal during some period, it will happen less frequently in the future, or that, if something happens less frequently than normal during some period, it will happen more frequently in the future

Question 32

What is the Law of small numbers?

Answer 32

exaggerated confidence in the validity of conclusions based on small samples.

• Misperceive a small sample to be indicative of the entire population

Question 33

What does the Sum of squared errors (SS) indicate?

Answer 33

The total dispersion, or total deviance of scores from the mean

Question 34

How does an increasing number of participants affect the distribution of the sample?

Answer 34

• Distribution becomes more normal

- Spread of the distribution decreases

Question 35

What do confidence intervals tell us?

Answer 35

There is a tradeoff between degree of certainty and width of the CI:

• The more certain you want to be, the wider (larger) the interval needs to be
• The goal is to have a high level of confidence paired with a small interval.
• One way to help achieve this is to have less variability in your sample (i.e. smaller error or mean)

Question 36

Sum of squares, Variance and standard deviation represent the same things.

What do they represent?

Answer 36

• The ‘fit’ of the mean to the data
• the variability in the data
• How well the mean represents the observed data
• error

Question 37

What does standard error tell you?

Answer 37

It is the standard deviation of the sampling distribution of a statistic

How accurate the mean of any given sample from that population is likely to be compared to the true population mean.

When the standard error increases, i.e. the means are more spread out, it becomes more likely that any given mean is an inaccurate representation of the true population mean.

Question 38

Which t-test has more power to find an effect given that everything else is equal?

Repeated measures

vs

independent measures

Answer 38

repeated measures t-test:

• When the same participants are used across conditions the unsystematic variance (often called the error variance) is reduced dramatically, making it easier to detect any systematic variance