Lecture 1

Question 1

What’s the framework for design and statistics?

Answer 1

1. Hypothesis/question
2. Propose a study
3. Design the study, i.e., how we get the data
4. Collection of data
5. Use of statistics to test hypothesis on model of data.
6. Examine and interpret the results

Question 2

Variable: Infinite Number of possible values, i.e., entities get a distinct score

A. Continuous variable
B. Categorical variable

Answer 2

A. Continuous variable

Question 3

Variable: Cannot take on all values within limits of the variable, i.e., entities are divided into distinct categories

A. Continuous variable
B. Categorical variable

Answer 3

B. Categorical variable

Question 4

Equal intervals on the variable represent equal differences in the property being measured.

A: Interval
B: Ratio
C: Nominal
D: Ordinal

Answer 4

A: Interval

Example: Difference between reaction time

Question 5

Variable has a clear definition of 0:0

A: Interval
B: Ratio
C: Nominal
D: Ordinal

Answer 5

B. Ratio

Example: Height

Question 6

Variable: Two or more categories.

A: Interval
B: Ratio
C: Nominal
D: Ordinal

Answer 6

C. Nominal

Example: One person one outcome, vegetarian/vegan/nonveg

Question 7

Variable: Categories have logical, incremental order

A: Interval
B: Ratio
C: Nominal
D: Ordinal

Answer 7

D. Ordinal

Example: Likert Scale, fail/pass/merit, ranked

Question 8

What’s the measurement area?

A: Validity (instrument measures what it set out to measure)
B: Reliability (ability of measure to produce same results under same conditions)
C: Both A and B
D: None of the above

Answer 8

C. Both A and B

The values have to have the same meaning over time and across situations, and must have systematic issues with measurements

Question 9

Variation: Differences in performance created by a specific experimental manipulation

A: Systematic variation
B: Unsystematic variation
C: Randomization variation
D: Other/NOTA

Answer 9

A. Systematic variation

Influencing the outcome based on a controlled experiment

Question 10

Variation: Differences in performance created by unknown factors

A: Systematic variation
B: Unsystematic variation
C: Randomization variation
D: Other/NOTA

Answer 10

B. Unsystematic variation

It influence results, but it’s not accounted for.

Example: Age, gender, IQ

Question 11

Variation: Minimizes unsystematic variation

A. Systematic variation
B: Unsystematic variation
C: Randomization variation
D: Other/NOTA

Answer 11

C. Randomization variation

It minimizes the impact of unsystematic variation

Question 12

What is the independent variable?

A. Predictor variable
B. Outcome variable
C. Both A and B
D. Neither

Answer 12

A. Predictor Variable

Also referred to as Hypothesized cause, manipulated variable.

Question 13

What is the dependent variable?

1. Measured variable
2. Manipulated variable
3. Both A and B
4. Neither

Answer 13

1. Measured variable

Also called outcome variable, and is the proposed effect.

Question 14

NHST

Which hypothesis predicts no effect of predictor variable on outcome variable?

1. Null hypothesis
2. Alternative hypothesis
3. Both 1 and 2
4. Neither

Answer 14

1. Null hypothesis

Question 15

NHST

Which hypothis says that there is an effect of the predictor variable on outcome variable?

1. Null hypothesis
2. Alternative hypothesis
3. Both 1 and 2
4. Neither

Answer 15

2. Alternative hypothesis

Question 16

NHST

What does NHST compute?
1. Probablity of null hypothesis being true
2. Probability of alternative hypothesis being true
3. Probablity of null hypothesis being false
4. None of the above

Answer 16

1. Probablity of null hypothesis being true

Also called P-value

Question 17

NHST

Which of the following is true?
1. Significant result does not mean effect is important.
2. Non significant result does not mean null hypothesis is true
3. Significant result doesn’t mean that the null hypothesis is false.
4. All of the above

Answer 17

4. All of the above

Question 18

NHST

Which of these refers to hypothesizing after the results are known?
1. p-hacking
2. harking
3. other

Answer 18

2. Harking

Question 19

NHST

What is p-hacking?

Answer 19

Selective reporting of significant results

Question 20

EMBERS

What is E?

Answer 20

Effect size
It is the standardized measure of how different the means are.

Example: Cohen’s d

Question 21

EMBERS

M - ?

Answer 21

Meta analysis
Rule of thumb for effect sizes
Funnel plots: value studies by their sample size and observe bias.

Question 22

EMBeRS

What is BeRS?

Answer 22

Baynesian approach, Registration, Sense

Question 23

Distribution

What is a normal distribution?

Answer 23

• Bell curve
• Symmetrical
• Two parameters
• Central tendency (mean)
• Standard deviation (dispersion)

Question 24

Symmetry and Skew

If mean = median = mode, then:
1. Symmetrical
2. Positive skew
3. Negative skew

Answer 24

1. Symmetrical

Skew is between -1 and 1

Question 25

# Symmetry and Skew Is mean > median > mode, then 1. Symmetrical distribution 2. Positive skew 3. Negative ske

Answer 25

2. Positive skew | x > +1

Question 26

# Symmetry and Skew Is mode > median > mean, then 1. Symmetrical distribution 2. Positive skew 3. Negative skew

Answer 26

3. Negative skew | x < -1

Question 27

# Kurtosis What does kurtosis look at? 1. Skewness 2. Shape of graph 3. Length of graph 4. Direction of graph

Answer 27

2. Shape of graph | Bulge or bend. If within +2 and -2, its all good!

Question 28

# Kurtosis Which is leptokurtosis? 1. k > 0 2. k < 0 3. k > +2 4. k < -2

Answer 28

1. k > 0

Question 29

# Kurtosis Which is platokurtosis? 1. k > 0 2. k < 0 3. k > +2 4. k < -2

Answer 29

2. k < 0

Question 30

# Tortium Quid/Third factor Which is NOT the assumptions of tortium quid do? 1. It helps infer causality 2. It has randomized controlled trials 3. It acts as a confounding variable/third factor 4. All of the above

Answer 30

4. All of the above ## Footnote It creates control settings to see how the 3rd controlled variable affects the data