Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

RC Nuggets > Stats > Flashcards

Stats Flashcards

1
Q

Measures of central tendency?

A

Mean
Median
Mode

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

Mean?

A

Average value

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

Median?

A

Middle value

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

Mode?

A

Frequent value

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Best measure when distribution not skewed?

A

Mean

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

Best measure when distribution skewed?

A

Median

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

Standard deviation?

A

Square root of variance

NOT influenced by sample size

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

Empirical rule of standard deviation?

A

Need a normal distribution (not skewed).

1 SD = 68% of data (34% on either side of mean)
2 SD = 95% of data
3 SD = 99.7% of data

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

Level 1 evidence?

A

Meta-analysis with small CI

At least TWO RCTs with a large sample size

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

RCTs can be in which level of evidence?

A

1, 2, 3 depending on sample size and how many done (need at least 2 for level 1)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

Meta-analysis can be in which level of evidence?

A

1,2 depending on the CI

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

Nul hypothesis

A

What we are trying to REJECT

Need p < 0.05 to reject (chance occurrence less than 5%)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

Type 1 error (ALPHA)

A

Nul hypothesis rejected but was TRUE

Error of INTERNAL VALIDITY

Probability of it happening = p value

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

Type 2 error (BETA)

A

Nul hypothesis accepted but was FALSE

Lack of POWER

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

Internal validity

A

Are the results representing what we wanted to measure?

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

Reliability

A

Are the results consistent and reproducible?

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

Student “t” test

A

Compares means of TWO samples made up of CONTINUOUS VARIABLES

Small samples with N < 30

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

ANOVA (“f” test)

A

Compares means of MORE THAN TWO samples made up of continuous variables

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

One-tailed test

A

Reject nul hypothesis in ONE direction (active treatment is better than placebo)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

Two-tailed test

A

Reject nul hypothesis in TWO directions (active treatment is different than placebo, either better or worst)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

“Z” test

A

SAME AS “T” TEST BUT FOR LARGER SAMPLES N > 30

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
22
Q

Chi-square

A

Evaluates ASSOCIATIONS between 2 samples of CATEGORICAL VARIABLES
(percentages, proportions)
Can compare 2 proportions
Can make a table of frequencies

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
23
Q

Pearson test

A

Test of linear correlation between CONTINUOUS VARIABLES
-1 = perfect indirect association
0 = no association
1 = perfect direct association

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
24
Q

Linear regression

A

PREDICTION of results once a correlation is demonstrated between CONTINUOUS VARIABLES

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
25
Q

Multiple logistic regression

A

PREDICTION of results once a correlation is demonstrated between CATEGORICAL VARIABLES

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
26
Q

Incidence

A

Number of new cases / number of people at risk over a certain period of time
(REMOVE KNOWN CASES)

27
Q

Prevalence

A

Number of NEW AND OLD cases / entire population over a certain period of time

28
Q

Establishing causality

A
  • High degree of correlation
  • Consistency of the correlation
  • Temporal association
  • Coherence with contemporary scientific knowledge
  • Dose-response relation
  • Reversibility
  • Biological plausibility
  • Specificity
  • Elimination of other explanations
29
Q

Relationship between prevalence and illness duration?

A

Prevalence is proportional to incidence x illness duration.

SO, if illness duration increases (ex: new treatment comes out which prolongs life expectancy), prevalence will ALSO INCREASE.

30
Q

Power

A

1 - Beta (error)

Probability that a difference will be picked up if that difference really exists.

Most important factor = SAMPLE SIZE

31
Q

Cohort study

A

Follow 2 groups (exposed/non-exposed) PROSPECTIVELY to see if they develop an illness.
CAUSE to EFFECT

Useful when EXPOSURE is rare

Longer and more expensive than case-control studies.

32
Q

Case-control

A

Looking back at 2 groups (ill / not ill) and determine their level of exposure.
RETROSPECTIVE
EFFECT to CAUSE

Useful when ILLNESS is rare

33
Q

Attrition bias

A

Loss of certain patients for analysis

Fix with LAST OBSERVATION CARRIED FORWARD

34
Q

Association in studies can be due to what?

A

Chance
Bias
Reverse causality
Confounding

35
Q

Qualities of a good screening test?

A 
Inexpensive
Easy to administer
Little discomfort
Reliable
Valid
Comparable to gold-standard
36
Q

PPV and NPV decedent on what?

A

PREVALENCE

37
Q

Sensitivity

A

If I HAVE THE DISEASE, will the test pick it up?

True positive / all people with disease

38
Q

Specificity

A

If I DON’T HAVE THE DISEASE, will the test not pick it up?

True negative / all people without disease

39
Q

Sensitivity good for?

A

RULING OUT

Negative result most helpful

40
Q

Specificity good for?

A

RULING IN

Positive result most helpful

41
Q

Positive predictive value

A

If I have a POSITIVE TEST, what are chances I have disease?

True positive / all positive test

42
Q

Negative predictive value

A

If I have a NEGATIVE TEST, what are chances I don’t have disease?

True negative / all negative test

43
Q

High sensitivity means?

A

Low false negatives.

44
Q

High specificity means?

A

Low false positives.

45
Q

Ratio vs. proportion vs. odd vs. rate

A

Ratio is dividing one number by another:

  1. Proportion is part/whole so like M : population or F : population
  2. Odd is part/non-part like M : F (part and non-part of whole population)

Rate is ratio with time as an intrinsic part of the denominator

46
Q

Odds

A

Part / non-part
Whole = 1
Non-part = 1-part

Odds = part / (1-part)

Odds = proportion / 1-proportion

47
Q

Odds if probability of death is 20%?

A 
Odds = 0.2 / (1-0.2)
Odds = 0.2/0.8
Odds = 0.25

25%
aka for every person that dies, there are 4 people who live

48
Q

Odds if probability of horse winning is 75%?

A 
Odds = 0.75 / (1-0.75)
Odds = 0.75 / 0.25
Odds = 3

300%
3 to 1 odds of your horse winning (3 x 100%)

49
Q

Disease odds ratio

A

Odd disease among exposed / odd disease among unexposed

50
Q

Exposure odds ratio

A

Odd exposure among disease / odd exposure among non disease

51
Q

Odds ratio

A

AD/BC

52
Q

Odds ratio relevant for which type of study?

A

Case-control (RETROSPECTIVE)

53
Q

Odds ratio interpretation?

A

OR > 1 means greater odds of association
(1.2 = 20% increase in odds of an outcome with a given exposure)

OR = 1 means no association between outcome and exposure

OR < 1 means lower odds of association
(0.2 = 80% decrease in the odds of an outcome with a given exposure)

54
Q

Risk ratio

A

Proportion so now we look at wholes!

Risk of disease among exposed / risk of disease among unexposed

Risk of disease among exposed = A / (A + C)
Risk of disease among unexposed = B / ( B + D)

55
Q

Risk ratio interpretation

A

RR > 1 means exposure is associated with a higher risk

RR = 1 means no association between exposure and disease

RR < 1 means exposure associated with lower risk (may be causally protective)

56
Q

Risk ratios relevant for which type of study?

A 
RCTs
COHORT studies (can be retrospective or prospective)
57
Q

Absolute risk reduction?

A

Risk of disease among exposure - risk of disease among non-exposure

Always positive because it is an absolute value

58
Q

NNT/NNH

A

1 / ARR (absolute risk reduction)

59
Q

Confidence interval

A

Mean +/- (1.96 x standard error of the mean)

Standard error of the mean = SD / (square root of sample size)

1.96 = t-statistic for 95% CI (most commonly used)

If CI crosses 1 = not statistically significant

60
Q

Effect sizes when comparing standardized practices?

A

Small 0.2
Medium 0.5
Large 0.8

61
Q

Effect sizes when comparing correlation coefficients?

A

Small 0.1
Medium 0.25
Large 0.4

62
Q

Effect sizes for relative risk?

A

Small 1.5
Medium 2.5
Large 4.3

63
Q

Pareto principle?

A

80% of effects are the product of 20% of the causes

Pareto diagram highlights the importance of different causes to a phenomenon but does NOT allow for evaluation of the quality of practice of doctors with regards to application of guidelines/recommendations!

RC Nuggets (20 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions