The SPINE of statistics

Question 1

What should the author stop doing?

Answer 1

Talking about his little boy penis

Question 2

What is the Spine of statistics?

Answer 2

Five key concepts to understand statistics:

• Standard error
• Parameters
• Interval estimates (confidence intervals)
• Null hypothesis significance testing
• Estimating the parameter

Question 3

What is meant by the fit of a model?

Answer 3

The degree top which a statistical model represents the data collected

Question 4

What is meant by parameters?

Answer 4

Parameters are the unknown values of an entire population, such as the mean and standard deviation

Question 5

What does the little hat denote in an equation?

Answer 5

An estimate

Question 6

What is meant by deviance/ error

Answer 6

The difference between a score and the mean

Question 7

When is the mean a good fit for the data?

Answer 7

When there isn’t much deviance

Question 8

What is a sampling distribution

Answer 8

Distribution showing the data of means ( or other parameters) from different samples

Question 9

When is a t distribution formed?

Answer 9

When the sample is relatively small (under 30) so the sampling distribution is not normal

Question 10

What value of t do we get for a 95% confidence interval?

Answer 10

Value of t for a two tails t test with a probability of 0.05, for the appropriate degrees of freedom

Question 11

What is an error bar and how is it marked?

Answer 11

An error bar shows the confidence interval on a graph and looks like the capital letter I

Question 12

What does it usually mean if two confidence intervals don’t overlap

Answer 12

they come from two different populations

Question 13

What can we infer if they come from two different populations from the experimental condition?

Answer 13

The experimental condition has induced a difference in the examples

Question 14

What two different approaches are there to the problem of how to use data to test theories?

Answer 14

1) Ronald Fishers idea of computing probabilities to evaluate evidence (P value)
2) Neyman and Pearsons idea of competing hypotheses (H0, H1)

Question 15

What is meant by the alpha?

Answer 15

The error rate/ significance level (95% etc)

Question 16

What is the difference between systematic and unsystematic variation?

Answer 16

Systemic variation is variation that can be explained by a model that we’ve fitted into the data (and therefore due to the hypothesis that we’re testing). Unsystematic variation is variation that cannot be explained by the model that we’ve fitted

Question 17

What is a test statistic mathematically?

Answer 17

Size of parameter/ sampling variation in the parameter

Question 18

What are type 1 and type 2 errors?

Answer 18

Type 1: Believing there is an effect in the population when there isn’t (false positive) (too small an interval)
Type 2: Believing there is no effect when there is (too wide an interval)

Question 19

When should you use a one tailed test?

Answer 19

When the opposite direction would result in the same as a non significant result

Question 20

What is meant by a and b levels?

Answer 20

a- The probability of type 1 error (0.05 in 95% significance level)
b- The probability of type 2 error

Question 21

If you wanted to measure three constructs in an experiment at a 95% significance level, assuming they’re independent, what would the probability of no type 1 error be?

Answer 21

95^3= 0.857

Question 22

How can we avoid this build up of error

Answer 22

Adjusting the level of significance so that it stays at 5%, this can be done by dividing a by the number of comparisons (tests)

Question 23

What is meant by the power of a test?

Answer 23

The statistical power of a test is its ability to find an effect

Question 24

How can the power of a test be described mathematically ?

Answer 24

1- B

Question 25

What statistical power do we typically aim for?

Answer 25

1- 0.2 = 0.8 (80%)

Question 26

What does the power of a test depend on?

Answer 26

1. The effect size
2. Level of significance (a level) - also recurring tests affect this
3. Sample size

Question 27

What can scientists do with this knowledge of calculating power?

Answer 27

1. Check if they have enough statistical power

2. Measure the sample size required

Question 28

What are the three guidelines regarding confidence levels and statistical significance?

Answer 28

1. 95% confidence intervals that just about just end to end represent a p-value for testing the null hypothesis of no differences of approximately 0.01
2. if there is a gap between the upper end of one 95% confidence interval and the lower end of another then p< 0.01
3. A p value of 0.05 is represented by a moderate overlap between the bars

Question 29

How is moderate overlap generally defined?

Answer 29

Half the length of the average margin of error (MOE is half the length of the confidence interval)

Question 30

What effect does the sample size have on the confidence intervals and why?

Answer 30

As the sample size gets bigger the interval gets smaller by the sd is divided by the square root of the sample size

Question 31

If we had a mean of 14 and the confidence interval ranged from 8-20 how would we report this?

Answer 31

M= 14, 95% Cl [8, 20]
M= 14 [8, 20]