Chapters 1 & 2 Definitions

Question 1

purported cause or predictor variable (things that vary and can be measured)

Answer 1

Independent Variable:

Question 2

possible effect (things that vary and can be measured)

Answer 2

Dependent Variable:

Question 3

All people in a defined setting or with certain defined characteristics. Can be clinical population including all people with aortic stenosis. Usually for epidemiologic studies of cause.

Answer 3

Population:

Question 4

A subset of people in the defined population. Usually for clinical research.

Answer 4

Sample:

Question 5

Reasoned judgment based on data that the sample resembles those of the population.

Answer 5

Inference:

Question 6

process at any stage of inference tending to produce results that depart systematically from the true values; any trend in the collection, analysis, interpretation, publication or review of data that can lead to conclusions that are systematically different from the truth.

Answer 6

Bias:

Question 7

occurs when comparisons are made between groups of patients that differ in ways other than the main factors under study (ones that affect the outcome of the study)

Answer 7

Selection Bias

Question 8

Occurs when the methods of measurement are dissimilar in different groups of patients.

Answer 8

Measurement Bias:

Question 9

Occurs when 2 factors are associated and the effect of one is confused with or distorted by the effect of the other. Occurs when one is trying to find out whether a factor is a cause of disease in and of itself.

Answer 9

Confounding Bias:

Question 10

The divergence of an observation on a sample from the true population value due to change alone.

Answer 10

Radom Variation:

Question 11

The degree to which the results of a study are correct for the sample of patients being studied. Measured by how well the design, data collection and analyses are carried out and is threatened by all of the biases and random variation.

Answer 11

Internal Validity:

Question 12

The degree to which the results of an observation hold true in other settings.

Answer 12

External Validity:

Question 13

Expresses the validity of assuming that patients in a study are similar to other patients. Does this particular study findings apply to my patient?

Answer 13

Generalizability:

Question 14

Occurs in categories without any inherent order. (Ex- characteristics determined by genes)

Answer 14

Nominal Data

Question 15

possess some inherent ordering or rank such as small to large or good to bad but the size of the intervals between categories is not specified. (Ex- 1+ - 4+ edema)

Answer 15

Ordinal Data:

Question 16

inherent order and the interval between successive values is equal no matter where one is on the scale.

Answer 16

Interval Data:

Question 17

Type of interval data; Takes on any value in a continuum, whether or not they are reported that way. (Ex- weight & serum chemistries; 193.54…. glucose is reported as 193)

Answer 17

Continuous Data

Question 18

Type of interval data; can take on only specific values and are expressed as counts (Ex- # of migraines the patient has per month).

Answer 18

Discrete Data:

Question 19

Nominal data that are divided into 2 categories. (present/absent; yes/no)

Answer 19

Dichotomous Data:

Question 20

The degree to which the data measure what they were intended to measure (AKA accuracy)

Answer 20

Validity:

Question 21

extent to which repeated measurements of a stable phenomenon by different people and instruments at different times and places get similar results. (AKA reproducibility and precision)

Answer 21

Reliability:

Question 22

all observations are subject to variation because of the performance of the instruments and observers.

Answer 22

Variation:

Question 23

describes the frequency distribution of repeated measurements of the same physical object by the same instrument.

Answer 23

Normal Distribution:

Question 24

Central tendency (middle of the distribution)- Sum of values for observations divided by the number of observations; advantage→ well suited for math manipulation; disadvantage→ affected by extreme values

Answer 24

Mean:

Question 25

Central tendency- The point where the number of observations above equals the number below. Advantage→ not easily influenced by extreme values; Disadvantages→ not well sited for math manipulation

Answer 25

Median

Question 26

Central tendency- Most frequently occurring value; Advantages→ simplicity or meaning; Disadvantage→ sometimes there are no or many, most frequent values

Answer 26

Mode:

Question 27

Dispersion- (how spread out the values are) From lowest to highest value in distribution; Advantages→ includes all values; Disadvantages→ greatly affected by extreme values.

Answer 27

Range:

Question 28

Dispersion- The absolute value of the average difference of individual values from the mean; Advantages→ well suited for math manipulation; Disadvantages→ for non-Gaussian distributions does not describe a known proportion of the observations.

Answer 28

Standard Deviation

Question 29

Occurs for purely statistical reasons, not because the patients have necessarily improved.

Answer 29

Regression to the Mean:

Question 30

2 basic properties of distributions used to summarize them

Answer 30

Central tendency + dispersion =