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Pcol2 > BIOSTATISTICS > Flashcards

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
Q

Simplest measure of variation
● Difference between the largest and the smallest
observations

A

RANGE

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26
Q

Average of squared deviations of values from the
mean

A

VARIANCE

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27
Q

How far each number from the data set is from the
mean

A

Variance

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28
Q

Heterogeneity or homogeneity among samples and Heterogeneity or homogeneity among samples

A

STANDARD DEVIATION

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29
Q

STANDARD DEVIATION unit follows that of

A

mean

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30
Q

How far the data set is from the mean

A

STANDARD DEVIATION

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31
Q

Shows variation relative to mean

A

COEFFICIENT OF VARIATION (CV)

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32
Q

Coefficient of Variation

A

(SD/mean) X 100

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33
Q

std dev =

A

square root of variance

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34
Q

variance =

A

mean / (n-1)

35
Q

Complete information of data can be presented

A

TABULAR

36
Q

Visual representation to display relationship

A

GRAPHICAL

37
Q

T/F: In graphical data presentaiton, Title must be stand alone

A

True

38
Q

Graph title should appear at the

A

bottom

39
Q

use sample data to make inferences (or
generalizations) about a population

A

INFERENTIAL STATISTICS

40
Q

Process of generalizing or drawing conclusions
about the target population on the basis of results
obtained from a sample.

A

INFERENTIAL STATISTICS

41
Q

collection of all possible
individuals, objects, or
measurements of interest.

A

POPULATION (N)

42
Q

portion, or part, of
the population of
interest

A

SAMPLE (n)

43
Q

a probability and is,
in reality, the probability of rejecting a true null
hypothesis.

A

LEVEL OF SIGNIFICANCE

44
Q

a statement about one or more
populations.

A

HYPOTHESIS

45
Q

Hypothesis of no
difference

A

NULL HYPOTHESIS

46
Q

What is being tested, in
which the decision is
being made - reject or
accept

A

NULL HYPOTHESIS

47
Q

(H0) Ideal situation

A

rejected

48
Q

T/F: If H0
is rejected, HA is
automatically accepted

A

True

49
Q

What we believe is true
if H0
is rejected

A

Alternative hypothesis

50
Q

Your expected conclusion, or what you hope to
conclude as a result of the experiment should be
placed in the

A

alternative hypothesis.

51
Q

T/f: The null hypothesis should contain an expression
of equality, either =, ≤ or ≥.

A

true

52
Q

is the hypothesis that will be
tested.

A

null hypothesis

53
Q

T/F; The null and alternative hypotheses are
complementary

A

true

54
Q

= stastical hypothesis

A

2 tail, 2 rejection regions

55
Q

≤

A

One tail, rejection
region on right

56
Q

≥

A

One tail, rejection
region on left

57
Q

sampled population or populations are at least
approximately normally distributed

A

PARAMETRIC

58
Q

procedures that test hypotheses that are not
statements about population parameters

A

NON-PARAMETRIC

59
Q

Used mainly on interval
and ratio scale data

A

PARAMETRIC

60
Q

Tend to need larger
samples

A

PARAMETRIC

61
Q

Data should fit a particular
distribution; data can be
transformed to that
distribution.

A

PARAMETRIC

62
Q

Samples should be drawn
randomly from the
population.

A

PARAMETRIC

63
Q

More powerful than
non-parametric equivalent

A

PARAMETRIC

64
Q

Less power than the
equivalent parametric
test

A

PARAMETRIC

65
Q

Can be used on data that
are not normally
distributed

A

NON-PARAMETRIC

66
Q

Can be used where the
samples are not selected
randomly

A

NON-PARAMETRIC

67
Q

Can be used on small
samples

A

NON-PARAMETRIC

68
Q

Can be used on ordinal
and nominal scale data

A

NON-PARAMETRIC

69
Q

procedures that make no assumption about the
sampled population

A

DISTRIBUTION-FREE

70
Q

A statistical measure of the strength of a linear
relationship between paired data.

A

PEARSON FORMULA OF CORRELATION

71
Q

Positive values denote

A

positive linear
correlation

72
Q

Negative values denote

A

negative linear
correlation

73
Q

A value of 0 denotes

A

no linear correlation

74
Q

The closer the value is to 1 or –1, the __________
the linear correlation.

A

stronger

75
Q

The farther the value is to 1 or –1, the __________
the linear correlation.

A

weaker

76
Q

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

A

Correlation

77
Q

If the absolute value of your correlation coefficient
is above the critical value, you ________ your null
hypothesis

A

reject

78
Q

If the absolute value of your correlation coefficient
were less than the critical value, you would fail to
________ your null hypotheses:

A

fail

79
Q

used for interval and nominal
data.

A

Point Biseria

80
Q

Used for Nominal to nominal
data.

A

Phi Coefficien

81
Q

between ordinal and another ordinal

A

Spearman’s

82
Q

measures if the difference is
significant or by chance.

A

Chi-square

83
Q
A

Pcol2 (20 decks)

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