Statistical Tests

Question 1

What does Relative Risk/Risk Ratio do?

Answer 1

● Measure of risk based on comparison of disease (or other health outcome)
incidence in two distinct groups

● Ratio of probability (percentage) of event occurring (or not occurring) in exposed
group vs. non-exposed group

● Negative exposure (toxin)= incident rate exposed/unexposed

● Positive exposure (prenatal care)= incident unexposed/exposed
Compares subgroup vs. entire population

● Commonly used in RCT’s and cohort studies

● RR of 1 means there were not differences between groups

● RR <1 means risk of bad outcome decreased
● RR >1 means risk of bad outcome is increased w/ intervention

Question 2

What are Odds Ratios?

Answer 2

Odds of disease between two groups (happening vs. not happening)

Estimate of association (estimates the RR)

Gambling

Compares odds of event in one group to odds in comparison group

Overestimates (this number is always higher than relative risk!!!)

Commonly used in case control studies in epidemiological research

Question 3

What do both RR and OR Ratios do?

Answer 3

Both compare the likelihood of an event to occur between two distinct groups

Question 4

How are RR and OR Ratios different?

Answer 4

RR is easier to interpret & is consistent with the general intuition; Comparison b/w subgroup & entire population.. rather than… subgroup & remained population

Question 5

Case-Control Studies in context of RR Ratios:

Answer 5

Case-control designs: limit RR calculation due to cases being selected on basis
of disease rather than exposure (RR compares exposed to unexposed)

Question 6

When is RR & OR Comparable?

Answer 6

When the studied is rare

Question 7

When does OR overestimate RR?

Answer 7

OR overestimates RR when the disease is more common → should be avoided if
RR can be used

Question 8

Hypothesis Testing:

What is Hypothesis?

Answer 8

Assumed proposition

Question 9

Hypothesis Testing:

What is Alternative Hypothesis?

Answer 9

Alternative Hypothesis is a Prediction that some observed difference is significant & due to knowable cause

~Statement there is a difference between groups not attributed to chance alone

~CAN accept the alternative hypothesis!

Question 10

Hypothesis Testing:

Null Hypothesis:

Answer 10

● Reject null hypothesis or fail to reject null hypothesis
YOU CANNOT ACCEPT A
NULL HYPOTHESIS (you can only find evidence against it)

● Prediction that observed difference is due to chance alone and not due to
systematic cause

● Statement of “no difference”

Question 11

How to Test the Null:

Answer 11

Type I and II Errors (used to test Null)

Type I: False positive (alpha)

Reality: no difference between groups

Study shows difference: error!

Study shows no difference: correct!

Type II: False negative (beta)

Reality: difference exist between groups

Study shows difference: correct!

Study shows no difference: error

Question 12

What is False Positive and False Negative?

Answer 12

False Positive = good quality item gets rejected

False Negative = poor quality item gets accepted

Question 13

What is Nominal?

Answer 13

Nominal: label or category without rank or order (mutually exclusive)

Ex. Male/female/dead/alive/pass/fail

Uses chi-square test

Tests difference in proportions

Question 14

What is Ordinal?

Answer 14

Ordinal: label or category with some meaningful order or sequence

Not measured- without definite boundaries/levels

Ex. Severity of disease

Question 15

What is Linkert Scale?

Answer 15

Linkert scale: strongly agree, agree, disagree, & strongly disagree

Uses Mann/Whitney

Tests differences in rank order

Question 16

What is Interval?

Answer 16

Interval: scaled measure with an arbitrary zero point (temperature)

Difference between levels is meaningful

T-test or ANOVA

Tests differences in means

Used for chemists i.e. so irrelevant (sorry chemists)

Question 17

What is Ratio?

Answer 17

Ratio: scaled measure with an absolute/true zero point(test score)

Question 18

Chi Square =

Answer 18

Nominal Data

Question 19

Mann-Whitney =

Answer 19

Ordinal Data

Question 20

ANOVA =

Answer 20

Interval/ratio with 3+ independent groups

Question 21

T-Test/paired T-Test =

Answer 21

Interval/Ratio w/ 2 Independent Groups

Question 22

Inferential Statistics:

Answer 22

….?

Question 23

Confidence Intervals:

Answer 23

Tells you most likely range of unknown population average

Most likely range within which the true size of effect lays

Confidence that if anyone reproduced this study they would have same results
due to independent variable(s)

Question 24

What 3 things Impact the width of CI (Confidence Interval)?

Answer 24

Confidence level: typically 95%

Variability: standard deviation

Sample size: smaller sample sizes generate wider intervals
P values do not affect

If the CI includes 1 the effect isn’t significant

Question 25

What is P Value?

Answer 25

-Tests likelihood of differences occurring by chance alone

Question 26

How is P level determined?

Answer 26

- P-level is predetermined probability researcher is willing to make a type I error - AKA “Alpha level”

Question 27

What does P < 0.5 mean?

Answer 27

-P < .05 means a 5% chance observed difference was due to chance & a 95% confidence that results were due to independent variable(s)

Question 28

What does P < 0.001 mean?

Answer 28

-P < .001 means a .1% chance observed difference was due to chance & a 99.9% confidence that results were due to the independent variable(s)

Question 29

What do we do with a P-Score equal to or less than 0.05?

Answer 29

P-SCORE EQUAL TO OR LESS THAN .05 MEANS WE REJECT NULL & | ACCEPT ALTERNATIVE

Question 30

What are the effects of Size in Statistics?

Answer 30

● Statistical significance (p-value) indicates difference between two groups ● Effect size describes magnitude of difference How much more effective? ● We can standardize difference and compare it to 0 ● Standard measure that can be calculated from various statistical outputs ● Standardized mean effect, expresses the mean difference between two groups in standard deviation units (Cohen’s d) Need mean and standard deviation for each group Values calculated for effect size range from -3 to 3 just like the standard deviation Standard interpretation: .8= large .5=moderate .2= small

Question 31

What is Experimental Event Rate (EER)?

Answer 31

EER = event rate in the treated/affected group

Question 32

What is Control Event Rate (CER)?

Answer 32

CER = event rate in the control/unaffected group

Question 33

What does Absolute Risk Reduction (ARR) do?

Answer 33

ARR: Compares treatment effectiveness (CER-EER) Difference in outcome rates between control and experimental groups Inverse of NNT How much does the treatment reduce the risk?

Question 34

What does Attributable Risk do?

Answer 34

measure of the prevalence of a condition or disease. Given a group of people exposed to a risk, it’s the fraction who develop a disease or condition. Put another way, AR is the cases that would be eliminated if the exposure were also eliminated. Opposite of ARR(EER-CER)<<

Question 35

What is Relative Risk Reduction (RRR)?

Answer 35

RRR = Percentage of original risk removed; Reduced Risk (ARR/CER) Not a good number/test (can inflate numbers or findings) “More or less likely to happen”

Question 36

Describe Number Needed to Treat-Prevent Bad Outcome:

Answer 36

Patients to treat for 1 to have a benefit Number of patients needed to prevent one additional bad outcome Ideal NNT=1 (this means everyone improves with treatment- higher is less effective and negative # indicates harmful) 1/ARR (inverse of absolute risk)

Question 37

CER =

Answer 37

control event rate: event rate in the control/unaffected group

Question 38

EER =

Answer 38

experiment event rate: event rate in treated/affected group

Question 39

AAR =

Answer 39

absolute risk reduction: compares treatment effectiveness (CER-EER) → How much does the treatment reduce the risk

Question 40

Sensitivity =

Answer 40

of people who have the disease and test +/ of people who have the disease Highly sensitive tests catch the disease every time: sometimes they are wrong (false +) Good at detecting/screening

Question 41

SnOUT =

Answer 41

(sensitive test if it is negative rules disease OUT) Probability that subject with the disease will screen positive for that disease ?? what do they mean exactly????

Question 42

Highly sensitive tests can catch the disease nearly every time-

Answer 42

rules OUT disease when the result is negative NOT dependent on populations Sometimes they are wrong (false +): say you have the disease and you really don’t Good at detecting & screening If you test negative you can be almost 100% sure you don’t have the disease

Question 43

Specificity =

Answer 43

of people who do not have the disease and test -/ of people who do not have the disease Highly specific tests are rarely wrong But sometimes they miss the diagnosis (false -) Good at being right/confirming the diagnosis Spin (specific test if it is positive rules a disease IN) ``` Sometimes miss the diagnosis (false -): say you don’t have the disease and you really do (bummer) ``` Good at being right/confirming the diagnosis *CANNOT HAVE A TEST THAT IS HIGH IN BOTH SENSITIVITY & SPECIFICITY; INVERSELY RELATED

Question 44

What is Positive Predictive Value?

Answer 44

Chance to have disease when test was positive Chance that when test is + you actually have the disease PV+: true positive/ (true positive + false positive)

Question 45

What is Negative Predictive Value?

Answer 45

Chance to NOT have disease when test was negative Chance that when test is – you actually do NOT have disease PV-: true negatives/ (true negatives + false negatives)

Question 46

Statistical Significance vs. Clinical Significance

Answer 46

Statistical- used in hypothesis testing Clinical- practical importance of a treatment effect- whether it has a real, palpable, noticeable effect ton daily life

Question 47

Describe Critical Thinking Competency:

Answer 47

ASK: convert information needs into a searchable question ACQUIRE: search with efficiency for best evidence to answer 
 APPRAISE: critically appraise evidence 
 APPLY: results in clinical practice 
ASSESS: evaluate clinical decision making skills