Brainscape's Knowledge GenomeTM


Top Flashcards (Certified)
All Flashcards

EDRA > 9. Statistics > Flashcards

9. Statistics Flashcards

(52 cards)

Start Studying
1
Q

Continuous

A

Continuous:

can take any value in a
given range
e.g. height or weight

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

Discrete:

A

Discrete:

can take an integer value only
e.g. visual analogue scale

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

Ratio:

A

Ratio:

a data series that has zero as its
baseline, such as heart rate and
temperature (degrees Kelvin)

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

Interval:

A

Interval:

a data series that has zero as a
point on a larger scale, such as
temperature (degrees Celsius)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q 
Numerical data (obtained from
measurements)
A

Continuous

Discrete:

Ratio:

Interval:

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

Categorical data (grouped data

A

Nominal (unordered)

Ordinal (ordered):

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

Nominal (unordered):

A

Nominal (unordered):

data comes from mutually exclusive
unranked groups, e.g. procedural outcome (success or
failure) and gender (male or female)

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

Ordinal (ordered):

A

Ordinal (ordered):

ranked groups, e.g. categorical pain
rating scale (mild, moderate and severe), ASA grade (class I,
II, III, IV, V)

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

Probability

A

Probability is the chance of occurrence of an event.

It has a value
between 0 and 1.

The probability density curves are used to describe the
distribution of data in the given population.

They can be of different types:

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

Types of distribution of data

A

normal (most important), binomial (value of 0 or 1) or Poisson
distribution.

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

Characteristics of a normal distribution:

A

also called the Gaussian distribution

describes the distribution of continuous variables

bell-shaped symmetrical curve

mean, median and mode are identical

tails do not touch the baseline

peaks if variance (standard deviation) is low

flattens if variance (standard deviation) is high.

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

Mean:

A

it is the sum of all the values, divided by the number of values.

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

Median

A

it is the point that has half the values above and half below.

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

Mode:

A

it is used when we need a label for the most frequently occurring event.

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

Standard deviation:

A

it indicates how much a set of values is spread

around the average. It is a measure of dispersion.

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

Standard error of the mean

A

: it is the standard deviation of the
sample-mean estimate of a population mean. It gives an idea of how
closely the estimated mean value (from the sample) is likely to
represent the true mean value (from the general population).

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

It is worth remembering that:

what are the % SD for 1 2 3

A

± 1 standard deviations includes 68.2% of the data

± 2 standard deviations includes 95.4% of the data

± 3 standard deviations includes 99.7% of the data.

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

Reliability

A

Reliability is the dependability of a test

(consistency and
reproducibility).

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

Precision

A

Precision is the extent to which random variability is absent from the
test.

Reliability of a test is dependent upon its precision.

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

Validity

A

Validity is the extent to which the test measures what it was designed
to measure.

It has two components: sensitivity and specificity.

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

Accuracy

Study These Flashcards
A

Accuracy is the ability of the test to produce the true values of the
measurements.

22
Q

Sensitivity

Study These Flashcards
A

The ability of a test to correctly
identify the individuals
who have the condition

A/(A + C)

23
Q

Specificity

Study These Flashcards
A

The ability of a test to correctly identify
the individuals who
do not have the condition

D/(B + D)

24
Q

False-positive rate

Study These Flashcards
A

Proportion of false positives
in the non-diseased population

B/(B + D)
or
(1 – specificity)

25
False-negative rate
Proportion of false negatives in the diseased population C/(A + C) Or (1 – sensitivity)
26
Positive predictive value
Proportion of true positives among all positives A/(A + B)
27
Negative predictive value
Proportion of true negatives among all negatives D/(C + D)
28
Accuracy
Proportion of true results (true positive and true negative) among all results (A + B)/(A + B + C + D)
29
having a lax criteria (three criteria – Point A) for diagnosis leads to:
more total positives and less total negatives high true positive (high sensitivity) high false positive (low specificity) low false negative low true negative
30
stringent criteria (four criteria – Point B) for diagnosis means:
less total positives and more total negatives low true positive (low sensitivity) low false positive (high specificity) high false negative high true negative.
31
Hence adding another criterion as a requirement for diagnosis will lead to
lower true-positive rate (low sensitivity), but a lower false-negative rate as well (higher specificity). The former situation is desirable in a screening test (high sensitivity), while the latter is desirable in a confirmatory test (high specificity).
32
Negatively skewed data
Most of the values are positive, tail points negatively (left) Mode is least changed, while mean the most Mean < Median < Mode Example: a very easy test will be high-scoring, so it will be negatively skewed
33
Positively skewed data
Most of the values are negative, tail points positive (right) Mode is least changed, while mean the most Mean > Median > Mode Example: a very difficult test will be poor-scoring, so it will be positively skewed
34
Comparison of case-control and cohort study Case-control study
Example: study of a group of chronic obstructive pulmonary disease patients (case) and without chronic obstructive pulmonary disease (control) to identify risk factors (smoking) Retrospective study Outcome is measured before exposure Inexpensive, easier, hospital-based Needs small sample size Allows estimation of odds risk only Used to study relatively rare conditions Selection bias more likely
35
Cohort study
Example: follow-up of smokers (cohort) and non-smokers (another cohort) and the development of chronic obstructive pulmonary disease in each cohort Prospective study Outcome is measured after exposure Expensive, harder, community-based Needs large sample size Allows determination of incidence and relative risk Used to study common conditions Selection bias less likely
36
Cross-sectional study
(prevalence study): studies present cases, allowing estimation of prevalence and risk factors at the same time. It is easy and inexpensive.
37
Randomised clinical trial:
is a prospective study with randomised study groups. It may be blinded to reduce selection bias.
38
Randomised clinical trial:
is a prospective study with randomised study groups. It may be blinded to reduce selection bias.
39
Relative risk is
a ratio of the probability of the event (disease) occurring in the exposed group versus a non-exposed group
40
Attributable risk
is the difference in rate of a condition between an exposed population and an unexposed population
41
The odds ratio
is the ratio of the odds of an event occurring in one group to the odds of it occurring in another group
42
Absolute risk reduction
is the reduction in risk associated with treatment (or removal of risk factor) as compared with placebo
43
Number needed to treat
is the average number of patients who need to be treated to prevent one additional bad outcome.
44
NNT
It is the inverse of absolute risk reduction. Example: number of children we need to vaccinate to prevent one case of disease. The lower the number needed to treat, the more effective the intervention is.
45
Number needed to harm
is the number of patients that need to be exposed to a risk factor over a specific period to cause harm in one additional patient.
46
NNH
It is the inverse of the attributable risk. Example: number of adults that need to be exposed to smoking to have one more case of lung cancer. The lower the number needed to harm, the worse the risk factor
47
Null hypothesis
States that there is no difference between the two groups being studied
48
Null hypothesis define
It provides a starting point for the study. Then, if no difference is found, it is accepted and there is no statistical difference noted. However, if a difference is noted, it is rejected and the result is ascribed a statistical significance.
49
The level of statistical significance is called
the P value (usually 0.05) and is the probability of occurrence of a type I error (α).
50
Type I error
Cause: rejecting null hypothesis when it is true Inference: finding a false difference between groups when none exists Probability of type I error is α
51
Type II error
Cause: accepting null hypothesis when it is false Inference: missing a true difference between groups Probability of type II error is β
52
Power of a study is described as
its ability to detect a difference between groups. It may be described as the probability of not obtaining a type II error (β). Hence, power = (1 – β). For a good study, Power should be 0.8 or more.

EDRA (26 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2025 Bold Learning Solutions. Terms and Conditions