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BIOSTATISTICS Flashcards

1
Q

TYPES OF VALIDITY

A

-internal
-external

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2
Q

The extent to which a
study establishes a
trustworthy
cause-and-effect
relationship between a
treatment and an
outcome

A

INTERNAL VALIDITY

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3
Q

Refers to how well the
outcome of a study can
be expected to apply to
other settings.

A

EXTERNAL VALIDITY

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4
Q

example of internal validity

A

methodology

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5
Q

example of external validity

A

results and
conclusion

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6
Q

Gives information that describes the data in some
manner

A

DESCRIPTIVE STATISTICS

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7
Q

T/f: Data is also described by compiling it into a graph,
table or other visual representation.

A

True

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8
Q

Uses a random sample of data taken from a large
population to describe and make inferences about
the population

A

INFERENTIAL STATISTICS

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9
Q

Analyze the sample to generalize the whole using
statistical tools

A

INFERENTIAL STATISTICS

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10
Q

From a population, you
get a sample to describe.

A

DESCRIPTIVE STATISTICS

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11
Q

When you represent the
population using a sample
to make a conclusion on
the population.

A

INFERENTIAL
STATISTICS

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12
Q

The smallest numbers
that can actually belong
to different classes

A

LOWER CLASS LIMITS

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13
Q

The largest numbers
that can actually belong
to different classes

A

UPPER CLASS LIMITS

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14
Q

Are the numbers used to separate classes, but
without the gaps created by class limits.

A

CLASS BOUNDARIES

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15
Q

MEASUREs OF CENTRAL TENDENCY

A

MEAN, MEDIAN, MODE

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16
Q

T/F: The disadvantage of using the mean as a measure
is that IT IS SENSITIVE TO UNUSUAL VALUES

A

MEAN

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17
Q

defined as the point in the distribution with 50% of
the measure on each side of it.

A

MEDIAN

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18
Q

Midpoint of the distribution

A

Median

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19
Q

T/f: Median is Not affected by extreme values

A

True

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20
Q

Value that appears most or most occurring value

A

Mode

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21
Q

T/F: Mode is Not affected by extreme values

A

True

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22
Q

Disadvantage of mode

A

difficult to use in a small sample of
continuous data

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23
Q

If there is one pea

A

UNIMODAL

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24
Q

If there are two peaks

A

BIMODAL

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25
Simplest measure of variation ● Difference between the largest and the smallest observations
RANGE
26
Average of squared deviations of values from the mean
VARIANCE
27
How far each number from the data set is from the mean
Variance
28
Heterogeneity or homogeneity among samples and Heterogeneity or homogeneity among samples
STANDARD DEVIATION
29
STANDARD DEVIATION unit follows that of
mean
30
How far the data set is from the mean
STANDARD DEVIATION
31
Shows variation relative to mean
COEFFICIENT OF VARIATION (CV)
32
Coefficient of Variation
(SD/mean) X 100
33
std dev =
square root of variance
34
variance =
mean / (n-1)
35
Complete information of data can be presented
TABULAR
36
Visual representation to display relationship
GRAPHICAL
37
T/F: In graphical data presentaiton, Title must be stand alone
True
38
Graph title should appear at the
bottom
39
use sample data to make inferences (or generalizations) about a population
INFERENTIAL STATISTICS
40
Process of generalizing or drawing conclusions about the target population on the basis of results obtained from a sample.
INFERENTIAL STATISTICS
41
collection of all possible individuals, objects, or measurements of interest.
POPULATION (N)
42
portion, or part, of the population of interest
SAMPLE (n)
43
a probability and is, in reality, the probability of rejecting a true null hypothesis.
LEVEL OF SIGNIFICANCE
44
a statement about one or more populations.
HYPOTHESIS
45
Hypothesis of no difference
NULL HYPOTHESIS
46
What is being tested, in which the decision is being made - reject or accept
NULL HYPOTHESIS
47
(H0) Ideal situation
rejected
48
T/F: If H0 is rejected, HA is automatically accepted
True
49
What we believe is true if H0 is rejected
Alternative hypothesis
50
Your expected conclusion, or what you hope to conclude as a result of the experiment should be placed in the
alternative hypothesis.
51
T/f: The null hypothesis should contain an expression of equality, either =, ≤ or ≥.
true
52
is the hypothesis that will be tested.
null hypothesis
53
T/F; The null and alternative hypotheses are complementary
true
54
= stastical hypothesis
2 tail, 2 rejection regions
55
≤
One tail, rejection region on right
56
≥
One tail, rejection region on left
57
sampled population or populations are at least approximately normally distributed
PARAMETRIC
58
procedures that test hypotheses that are not statements about population parameters
NON-PARAMETRIC
59
Used mainly on interval and ratio scale data
PARAMETRIC
60
Tend to need larger samples
PARAMETRIC
61
Data should fit a particular distribution; data can be transformed to that distribution.
PARAMETRIC
62
Samples should be drawn randomly from the population.
PARAMETRIC
63
More powerful than non-parametric equivalent
PARAMETRIC
64
Less power than the equivalent parametric test
PARAMETRIC
65
Can be used on data that are not normally distributed
NON-PARAMETRIC
66
Can be used where the samples are not selected randomly
NON-PARAMETRIC
67
Can be used on small samples
NON-PARAMETRIC
68
Can be used on ordinal and nominal scale data
NON-PARAMETRIC
69
procedures that make no assumption about the sampled population
DISTRIBUTION-FREE
70
A statistical measure of the strength of a linear relationship between paired data.
PEARSON FORMULA OF CORRELATION
71
Positive values denote
positive linear correlation
72
Negative values denote
negative linear correlation
73
A value of 0 denotes
no linear correlation
74
The closer the value is to 1 or –1, the __________ the linear correlation.
stronger
75
The farther the value is to 1 or –1, the __________ the linear correlation.
weaker
76
an effect size and so we can verbally describe the strength of the correlation using the guide that Evans (1996) suggests for the absolute value of r
Correlation
77
If the absolute value of your correlation coefficient is above the critical value, you ________ your null hypothesis
reject
78
If the absolute value of your correlation coefficient were less than the critical value, you would fail to ________ your null hypotheses:
fail
79
used for interval and nominal data.
Point Biseria
80
Used for Nominal to nominal data.
Phi Coefficien
81
between ordinal and another ordinal
Spearman’s
82
measures if the difference is significant or by chance.
Chi-square
83

Pcol2 (20 decks)

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