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Critical Inquiry > Session 5 - Confidence Intervals > Flashcards

Session 5 - Confidence Intervals Flashcards

1
Q

how is a 95% CI estimated?

A

by calculating 2 SE around the obtained value

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2
Q

What happens to the CI if the SD increases?

A

the CI increases.

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3
Q

What happens if the low range of a CI is a negative number?

A

then the finding can not be statistically significant because the null value lies in the CI. it’s like saying you are not 95% that the treatment works since a negative value indicates that it does NOT work.

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4
Q

why does the mean value or point estimate always fall within th 95% CI?

A

because the CI is constructed around the point estimate.

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5
Q

what does a broader CI reflect?

A

Less certainty or more potential error in your “best estimate”
more likely that the null will be in the interval

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6
Q

what does the curve width reflect?

A

the precision of the estimate

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7
Q

greater standard deviation or standard error=

A

greater variance among subjects.

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8
Q

more subjects=

A

greater approximation to the population=

more certainty of study value.

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9
Q

increased sample size=

A

decreased width of CI

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10
Q

what does a narrower CI reflect?

A

greater confidence in the point estimate.

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11
Q

a smaller effect size=

A

a greater likelihood that the null will be in the CI

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12
Q

Confidence Intervals are affected by

A

Variance
Sample size
Magnitude of effect (effect size)

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13
Q

what happens to the CI if you want to go from 95% to 99%?

A

the interval widens to increase the likelihood that the actual value lies within the range

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14
Q

what is the problem with having a wider CI?

A

it’s more likely that the null value will fall within the range and is therefore less likely to be statistically significant.

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15
Q

why would CIs be preferable to a p-value?

A

better visualization of possible fluctuation
doesn’t decide (dichotomize)like a p-value
gives you everything a p-value does plus more.

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16
Q

Power

A

how sure you can be that you will be able to rule out chance as an explanation for your findings.
Power=1-beta

17
Q

do you estimate power before or after an experiment?

A

after

18
Q

Do you estimate power when you have a null result?

A

yes.
you want to find out if it is a true null; false negative or true negative
what was the probability of a statistically significant result

19
Q

power is a function of

A 
(magnitude of effect)x
alpha error (type-I risk)x
sample size)/variance
20
Q

what is a commonly accepted level of power?

A

80%

21
Q

does 100% power mean that you will find the effect in your sample?

A

no.

100% power means that IF the effect you’re looking for is there, it’s likely to be statistically significant.

22
Q

what is a limitation to absolute effect size?

A

it does not permit comparison of findings on a given intervention between 2 or more studies.

23
Q

Standardized effect size

A

brings the absolute effect sizes of studies into a common set of units
only for parametric data where different outcome measurements were used.

24
Q

how do you compare effect size for dichotomous data?

A 
ARR - absolute risk reduction
and 
RRR - relative risk reduction
permits comparison of effects between studies with non-parametric outcome data. 
requires that data be dichotomized
25
Q

Event Rate

A

the proportion of patients in a group in whom “the event” is observed.
requires that the outcome be dichotomized.

26
Q

( absolute risk reduction ) ARR

A

ARR=CER-EER or (ARR=EER-CER in PT used to measure “improvement” attributed to the treatment)
CER=control’s even rate
EER=experimental event rate

27
Q

RRR (relative risk reduction)

A

the proportion of adverse events that would have occurred in the control group that are avoided by the intervention.
RRR=(EER-CER)/EER

28
Q

NNT

A

the number of patients that need to be treated to prevent one additional adverse outcome.
the number of patients you need to treat before a therapist can be sure that one patient improved who would not have improved without the intervention.

29
Q

NNT is the inverse of what?

A

NNT=1/ARR

30
Q

odds ratio (estimate of association)

A

estimate of the increased risk of “disease” given “exposure”
requires dichotomizing the outcome variable
usually obtained from a case-control study.
OR=Odds of disease amond exposed/odds of disease among non-exposed.
OR=AD/BC

31
Q

A=

A

diseased and exposed

32
Q

B=

A

non-diseased and exposed

33
Q

C=

A

non-exposed and diseased

34
Q

D=

A

non exposed and non diseased.

35
Q

what is the null value for an odds ratio?

A

1

Critical Inquiry (4 decks)

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