Introductory Statistics Flashcards

1
Q

null hypothesis

A

prediction that the observed difference is due to chance alone and not due to a systematic cause

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2
Q

null hypothesis is the statement of

A

no difference

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3
Q

statistics provide the evidence to

A

reject or fail to reject the null

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4
Q

alternative hypothesis

A

prediction that some observed difference is significant and due to some knowable cause

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5
Q

alternative hypothesis is the statement that

A

there is a difference between groups not attributable to chance alone

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6
Q

what does statistic testing test?

A

the likelihood of differences occurring by chance alone

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7
Q

p level

A

the predetermined probability researcher is willing to make a type 1 error

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8
Q

p level is also referred to as the

A

alpha level

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9
Q

p

A

“Only 5% of the time, this difference will be observed due to chance alone”

5% chance observed difference was due to chance
95% confident that results were due to independent variable(s)

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10
Q

p<0.001

A

.1% chance observed difference was due to chance

99.9% confident that results were due to independent variable(s)

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11
Q

nominal

A

label or category without rank

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12
Q

ordinal

A

label or category with some meaningful order or sequence

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13
Q

interval

A

scaled measure with an arbitrary zero point

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14
Q

ratio

A

scaled measure with an absolute zero point

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15
Q

inferential statistics don’ts (3)

A

◦Prove cause and effect
◦Estimate clinical effectiveness
◦Estimate risk/benefit

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16
Q

inferential statistics do’s (2)

A

◦Estimate the probability of getting results due to chance

◦Suggest numerical differences

17
Q

Statistical significance (p-value) indicates

A

difference between two groups

18
Q

Effect size describes the

A

magnitude of difference

◦How much more effective?

19
Q

We can standardize difference and compare it to

A

0

20
Q

effect size

A

standard measure, can be calculated from various statistical outputs

21
Q

Standardized mean effect, expresses the mean difference between

A

two groups in standard deviation units (Cohen’s d)

◦Need mean and standard deviation for each group

22
Q

values for effect size range from

A

-3 to 3

same as standard dev

23
Q

standard interpretation of effect size
.8=
.5=
.2=

A
◦.8 = large (8/10 of a standard deviation unit)
◦.5 = moderate (1/2 of a standard deviation)
◦.2 = small (1/5 of a standard deviation)
24
Q

relative risk

A

A measure of risk based on a comparison of disease (or other health outcome) incidence in two distinct groups

25
Q

what is relative risk the ratio of?

A

the probability of the event occurring in the exposed group versus a non-exposed group

26
Q

odds ratio

A

Comparing the odds of an event in one group to the odds of an event in some comparison group

27
Q

odds ratio estimates

A

association

28
Q

Both the odds ratio and relative risk compare the

A

likelihood of an event occurring between two distinct groups

29
Q

are relative risk or odds ratio easier to interpret?

A

RR is easier to interpret and consistent with the general intuition◦comparison between subgroup and entire population, rather than subgroup and remainder of population

30
Q

Case-control designs limit

A

RR calculation

cases selected on basis of disease rather than exposure (RR compares exposed to unexposed)

31
Q

OR and RR are comparable when the disease studied is

A

rare

32
Q

OR overestimates RR, when the disease is more

A

ccommon

◦should be avoided if RR can be used

33
Q

sensitivity vs specificity

A

Sensitivity is the proportion of patients with the disease who test positive

Specificity is the proportion of patients withoutthe disease who test negative

34
Q

confidence interval tells you

A

the most likely range of the unknown population average

35
Q

what three things impact the width of a confidence interval

A

◦Confidence level: typically 95%
◦Variability: standard deviation
◦Sample Size: Smaller sample sizes generate wider intervals