Flashcards in Chapter 3 - Probability Deck (16):

1

## Sample space

### the sample space is the set of all possible outcomes

2

## Event

### an event is any set of outcomes of interest

3

## Probability of an event

### the probability of an event is the relative frequency of the event over an indefinitely large (or infinite) number of trials

4

## Theoretical probability models

###
- theoretical probability models may also be constructed

- comparing empirical probabilities with theoretical probabilities enables us to assess the goodness-of-fit probability models

- this is an example of statistical inference

5

## Mutually exclusive

### - the two events A and B are mutually exclusive if they cannot both happen at the same time

6

## Positive predictive value

### the positive predictive value (PV+) of a screening test is the probability that a person has a disease given that the test is positive

7

## Negative predictive value

###
the negative predictive value (PV-) of a screening test is the probability that a person does not have a disease given that the test is negative

8

## Sensitivity

### the probability that the symptom (test) is present given that the person has a disease

9

## Specificity

### the probability that the symptom is not present given that the person does not have a disease

10

## False negative

### negative test result when the disease or condition being tested for is actually present

11

## False positive

### positive test result when the disease or condition being tested for is not actually present

12

## Bayesian inference

###
- it is an alternative definition of probability and inference

- it rejects the idea of the definition of probability sometimes called the frequency definition of probability (a theoretical concept)

- conceives two types of probability = prior probability and posterior probability

13

## Prior probability

###
- best guess by the observer of an event’s likelihood in the absence of data

- this may be a single number, or a range of likely values, perhaps with weights attached to each possible value

14

## Posterior probability

###
- the likelihood that an event will occur after collecting some empirical data

- it is obtained by integrated information from the prior probability with additional data related to the event in question

15

## Prevalence

### the probability of currently having the disease regardless of the duration of time one has had the disease

16