Chapter 6

Question 1

Lying with statistics

Answer 1

The intentional misapplication of statistical methods

Question 2

Statistical methodology

Answer 2

Justification of the choice of using a particular statistical method

Question 3

Descriptive statistics

Answer 3

In descriptive statistics, one aims to display data and conclusions accurately

Question 4

Inferential statistics

Answer 4

In inferential statistics, one aims to draw a justified conclusion from data

Question 5

Stochastic hypothesis

Answer 5

A hypothesis whose implications come in the form of a probability distribution

Question 6

Deterministic hypothesis

Answer 6

A hypothesis all of whose implications are certain

Question 7

Quantitative measure of measurement error

Answer 7

The likelihood of a measurement error being made, presented on a quantitative scale

Question 8

Error based statistics

Answer 8

Determining the probability of an observation given that a certain hypothesis is true

Question 9

Confidence in a hypothesis

Answer 9

The subjective estimation of the probability of a hypothesis

Question 10

Fisher’s significance testing

Answer 10

A method of statistical hypothesis testing developed by Ronald Fisher

Question 11

Test statistic

Answer 11

Any quantity, computed from values in a sample, that is considered for a statistical purpose

Question 12

Sampling distribution

Answer 12

A distribution over the possivle outcomes of the test statistic

Question 13

p-value

Answer 13

The probability of observing an outcome at least as extreme as the observed outcome

Question 14

Significance level

Answer 14

A conventionally set lever for p-values, below which the associated hypothesis should be rejected

Question 15

p-value abuse

Answer 15

Changing test setup, statistical method, or sample in order to make the p-value either higher or lower than the significance level (depending on what result is desired)

Question 16

Neyman-Pearson hypothesis testing

Answer 16

A method of hypothesis testing developed by Jerzy Neyman and Karl Pearson

Question 17

Original hypothesis (Hi)

Answer 17

Some claim that you are interested in

Question 18

Alternative hypothesis (Ha)

Answer 18

A hypothesis that due to logical necessity has to be true if the original hypothesis is false and vice versa. I.e. Ha is the inverse, or negation, of Hi

Question 19

Type I error

Answer 19

Wrongly rejecting a true hypothesis Hi

Question 20

Type II error

Answer 20

Wrongly accepting a false hypothesis Hi

Question 21

Power of a test

Answer 21

The probability of correctly rejecting a false hypothesis Hi

Question 22

Bayesian statistics

Answer 22

Posterior probability of a hypothesis is calculated based on the prior probabilities for this hypothesis together with the observed outcome, using Bayes’ theorem

Question 23

Prior probability

Answer 23

The (estimated) probability of the hypothesis being true before the application of Bayes’ theorem

Question 24

Subjective degrees of belief

Answer 24

The Bayesian view of what is meant by “probability” - that probability is the subjective estimation of likelihood rather than a property belonging to the world

Question 25

Posterior probability

Answer 25

The (calculated) probability of the hypothesis being true after the application of Bayes theorem

Question 26

The problem of priors

Answer 26

Bayesianism does not offer a clear way to determine prior probabilities

Question 27

The principle principle

Answer 27

A subject’s prior probability should be assigned on the basis of objective probability, if it is known

Question 28

The principle of indifference

Answer 28

A subject’s prior probabilities should be assigned equally to the possible outcomes, if there is no information about the objective probabilities

Question 29

The problem of slow convergence

Answer 29

If two subjects assign sufficiently different prior probabilities to the same hypothesis, it is possible that their respective posterior probabilities will not converge even though Bayes’ theorem has been applied to large amounts of data

Question 30

The problem of old evidence

Answer 30

The problem of determining what evidence that has been previously used to determine posterior probabilities

Question 31

The problem of uncertain evidence

Answer 31

Bayesianism does not take uncertainty about evidence into account