Sampling, data description & probability

Question 0

Population (target population)

Answer 0

The totality of subjects about which we want to make inference

Question 1

Biostatistics

Answer 1

Application of statistical methods to medical and biological problems

Question 2

Sample

Answer 2

The subset of the population on which data is actually collected

Question 3

Sampling error

Answer 3

The difference between the sample result and the true underlying population value. Error may be caused due to bias (sampled subjects are not representative of the population) or random variation (variation due strictly to chance, even with unbiased selection of subjects)

Question 4

Sample of convenience

Answer 4

Take who you can get. Easy to obtain and bias may be a problem

Question 5

Random sample

Answer 5

Every individual in the population has an equal chance of being in the sample. Used to ensure that uncontrolled factors do not bias results. May be difficult to obtain

Question 6

Stratified sampling

Answer 6

Sample is drawn within each of two or more strata (groups with common characteristics). Used to improve accuracy in certain circumstances

Question 7

Data variables

Answer 7

The measurement or observation made of the sampled subjects

Question 8

Categorical

Answer 8

Values fit into natural categories. Ex. Gender, disease status, vital status

Question 9

Discrete variable

Answer 9

Ordered numerical data restricted to integer values (count data). Ex. Number of siblings, # of days hospitalized

Question 10

Continuous variable

Answer 10

Numerical data that can take on any value. Often limited by precision of measuring instrument. Ex. Age, height, weight, cholesterol

Question 11

Descriptive statistics

Answer 11

Part of statistical methods that deals with organizing and summarizing data

Question 12

Frequency distribution

Answer 12

A table of categories along with their observed frequencies. Categories may be natural (gender, race) or they may be crated from continuous variables by grouping values together (21-49 years old)

Question 13

Histogram

Answer 13

Graphical representation of a frequency or relative frequency distribution. Used to determine shape of a distribution of data

Question 14

Mean

Answer 14

Sum of all observations divided by n, the number of subjects

Question 15

Median

Answer 15

Half of the values are below the median and half are above. The middle most observation of ordered data

Question 16

Mode

Answer 16

The most frequently occurring observation in the sample

Question 17

Range

Answer 17

The difference between the highest and lowest observation

Question 18

Standard deviation

Answer 18

A measure of the average distance of each observation from the mean

Question 19

Normal distribution

Answer 19

Widely used distribution with a bell shape. Mean, median and mode are equal. 68% of its area lies within 1 std dev of mean. 95% of its area lies within 1.96 std dev of mean. 99% of its area lies within 2.58 std dev of mean

Question 20

Confidence interval

Answer 20

Also called confidence limits. An interval that describes where the population mean is likely to be with a certain level of confidence (usually 95%). Formula: mean +/- (1.96)(SEx)

Question 21

Standard error of the mean

Answer 21

Computed as SEx= s/n^1/2

Question 22

Set

Answer 22

Collection of distinct objects (ex. Sample of patients)

Question 23

Event

Answer 23

A characteristic defining a subset of our set (ex. Affliction with a disease)

Question 24

Pr(event)

Answer 24

Probability of the event, estimated as (# experiencing event)/(#in the set)

Question 25

Conditional probability

Answer 25

Let e1 and e2 be two events. The conditional probability of e1 given e2 is expressed as Pr(E1|E2) = probability of e1 and e2 occurring jointly/probability of e2 occurring

Question 26

General multiplication rule

Answer 26

Pr(E1 and E2) = Pr (E1|E2) x Pr(E2)