Tipc: Test 2 : Working With & Interpreting Study Data

Front 1

define variable

what does it generate?

Back 1

measures of a single characteristic that can vary
Characteristics of a population or person vary (e.g., blood pressure)
These characteristic measures generate study data

Front 2

if you are measuring the amount of caffiene a person is exposed to, and the outcome of pancreatic cancer, what are caffinene and cancer as far as variables?

Back 2

Caf: Independent variable

Cancer: dependent variable

Front 3

what is an independent variable?

Back 3

Set/determined by Investigators

exposure/intervention

Front 4

what is a dependent variable?

Back 4

The effects that depend upon the independent variable(s)


the outcome

Front 5

what re the 2 main types of variables (not dep/indep)

Back 5

Qualitative: descriptions (words, not numbers) of variable features
Quantitative: uses rigid, continuous measurement scale

Front 6

what is a qualitative variable

Back 6

descriptions (words, not numbers) of variable features

Front 7

what is a Quantitative variable

Back 7

uses rigid, continuous measurement scale

Front 8

Nominal variable types have low or high information content?

Back 8

LOW

Front 9

example of a nominal variable

Back 9

Blood type; color = cyanotic or jaundiced; taste = bitter or sweet

Front 10

binary variables are what? (example plz)

Back 10

Sex; heart murmur = present/absent

Front 11

please give an example of continuous variable data and tell if it has high or low information content.

Back 11

Temperature (Fahrenheit)


second highest info content

Front 12

please give an example of ratio variable data and tell if it has high or low information content.

Back 12

Temperature (Kelvin); blood pressure

Highest info content

Front 13

rank the following in order from lowest to highest information content:

nominal, ordinal, binary, ratio, continuous

Back 13

nominal
binary
ordinal
continuous
ratio

Front 14

what is the goal of statistical methods?

Back 14

explain variation

Front 15

what is the mean (mu)

Back 15

AVERAGE

would be the middle of the bell curve

Front 16

what is the mode?

Back 16

measurement value that occurs most frequently

Front 17

median

Back 17

equal number of variables above and below this point

Front 18

what is central tendency?

Back 18

central feature of the data

mean, mode, median

Front 19

Mean

Back 19

the average value

Front 20

Mode

Back 20

most commonly observed value

Front 21

Percentiles (quantiles)

Back 21

percentage of observations below the indicated point when all the observations are ranked in descending order
Median = 50th percentile

Front 22

Range

Back 22

lowest to highest value

Front 23

Variance ** ON TEST

Back 23

measure of variability of data about the mean (sum of squared deviations from the mean, or s2)

Front 24

Skewness

Back 24

has to do with horizontal distribution of data (wider than it should be)

Horizontal stretching of a frequency distribution to one side or the other  creates one long, “thin” tail to the data distribution

Front 25

Kurtosis

Back 25

has to do with vertical distribution of data (too tall)

Vertical stretching or flattening of the frequency distribution

Front 26

when looking at a bell curve and determining skew, what do you look at
THIS IS ON THE TEST

Back 26

which side below the highest point has more values

SLIDE 17 is SKEWED LEFT

Front 27

Deductive

Back 27

info known from something general and then applying it to something specific

proceed from the general to the specific
Features of a larger population or general scenario are applied to a specific person or situation

Front 28

Inductive

Back 28

take specific info general population (this is what epidemiology does)

proceeds from the specific to the general
Seeks to find general principles and valid generalizations from specific data

Front 29

What type of reasoning is the following?

All Americans Love Democracy

People who Love Democracy Vote

John Doe is an American

SO

John Doe Loves Democracy and Votes

Back 29

Deductive Reasoning

Front 30

What type of reasoning is the following?

Ben, a 78 yo man, has a systolic BP of 140 mm Hg

Hank, a 44 yo man, has a systolic BP of 128 mm Hg

Bob, a 21 yo man, has a systolic BP of 116 mm Hg


SO
Systolic BP in men increases with age

Back 30

Inductive Reasoning

Front 31

what is a hypothesis?

Back 31

predictions about what an examination of appropriately collected data will show

Front 32

caffeine consumption increases the risk of pancreatic cancer..

this is an example of what?

Back 32

a hypothesis

Front 33

what is a Null hypothesis (H0)

Back 33

there is no statistically significant difference between two groups

Front 34

People who consume more caffeine are not at greater risk of developing pancreatic cancer than those who consume less caffeine

this is an example of what?

Back 34

a null hypothesis

Front 35

what is an alternative hypothesis? (H alpha)

Back 35

there is a statistically significant difference between two groups

Front 36

People who consume caffeine are at greater risk of developing pancreatic cancer than people who do not

this is example of a what?

Back 36

Alternative hypothesis (Ha):

Front 37

Statistical analyses test what?

Back 37

the null hypothesis

Front 38

using a 2x2 table please fill it in according to hypothesis testing

Back 38

see slide 27

TOP: H0 is true | H0 is false
Vertical : Fail to reject H0
Reject H0

A=correct
B=type II error (beta)
C=Type I error (alpha)
D=correct

Front 39

what is alpha? usually set at?

Back 39

= Type I error = the probability of incorrectly rejecting the H0
alpha is usually set at 0.05

Front 40

what is Beta?

Back 40

= Type II error = probability of failing to reject the null hypothesis when it is false

Front 41

type II error is =?

Back 41

Beta

= probability of failing to reject the null hypothesis when it is false

Front 42

type I error is =?

Back 42

alpha

the probability of incorrectly rejecting the H0
alpha is usually set at 0.05

Front 43

what is the p value

Back 43

probability that the 2 groups ARE NOT DIFFERENT

anything less than .05 then you reject null hypothesis

Front 44

what is 95% Confidence Interval (CI)

Back 44

For normally distributed data, the mean ± 1.96 SE (standard error) estimates the range within which 95% of means of repeated measures would be expected to fall --> this range is the 95% CI

Front 45

If RR= 1 what does this mean?

Back 45

there is no differentiation in having the disease and not

rr of disease/ rr of not disease

with both exposed


no comparison, not significant

Front 46

if RR= below 1 what does this mean

Back 46

greater chance of being exposed and NOT having disease

Front 47

if RR= is greater than one

Back 47

you have greater chance of being diseased with exposure

disease/no disease

Front 48

REVIEW SLIDE 30

Back 48

it was confusing

Front 49

have RR= 1.8 +/- .92

what is your mean?

Back 49

1.8

Front 50

have RR= 1.8 +/- .92

what is your 95% CI range?

Back 50

.88 - 2.72

basically add/subtract .92 from 1.8

Front 51

if the value of 1 falls within the 95% range, what does this mean?

ON TEST

Back 51

your information is not statistically significant

the value of 1 has to exist in either the positive or negative tail

so if you have have RR= 1.8 +/- .92

this means 1 falls within that range, and isn't significant

Front 52

if you have a 95% CI of 1.3-1.6 what does this mean?

Back 52

you can reject the null hypothesis

so your variable does cause the outcome

Front 53

what is the definition of Number Needed to Treat (NNT)

Back 53

Guess what, it doesn't matter cuz it is not on the test! BOOM