This document provides an introduction to biostatistics. It defines key biostatistics terms like data, variables, scales of measurement, and methods of data presentation. Descriptive and inferential statistics are introduced. Common measures of central tendency (mean, median, mode) and dispersion (range, standard deviation, variance) are defined for different data types. Common methods for presenting data visually, like histograms, bar graphs and box plots, are also described. The normal distribution is introduced as an important assumption for many statistical tests. Examples are provided to illustrate concepts like using z-scores to determine what proportion of values fall above or below a given cutoff from the mean.

1. AN INTRODUCTION ON
BIOSTATISTICS PART 1
Dr. Vaneet Aggarwal
Department of Pharmacology
1

2. TOPICS :
Definitions
Data and types of data
Methods of data presentations
Types of stats
Measure of central tendency
Measure of dispersions
The normal distribution
2

3. WHAT DOES
STATISTICS
MEAN ???
3

4. STATISTICS: Principles and methods for collection,
presentation, analysis and interpretation of data.
BIOSTATISTICS: Tools of statistics applied to the data that
is derived from biological sciences.
4

5. WHY DO WE NEED
BIOSTATISTICS ?
To define normalcy.
To study the correlation or association between two or more
attributes.
Locate define and measure the extent of the disease.
To evaluate the efficacy of drugs.
To determine the success or failure of health care program.
5

6. QUALITY
RESEARCH
How does biostatistics help a post graduate
student ?
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7. DESCRIPTIVE AND
INFERENTIAL STATISTICS
Descriptive stats are concerned with the presentation,
organisation, and summarization of data.
Inferential stats allow us to generalise from our sample
group of data to a larger group of subjects.
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8. VARIABLES
A variable is simply what is being observed or measured.
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9. FLAVOURS OF VARIABLES
DEPENDENT variable is the outcome of interest, which
should change in response to some intervention.
INDEPENDENT variable is the intervention, or what is being
manipulated.
9

10. More generally if one variable changes in response to
another, we say that dependent variable is the one that
changes in response to independent variable.
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11. DATA
11
Collective
recording of
observations.

12. 12
Depending
upon
source of
data
collection
PRIMAR
Y
DATA
SECONDAR
Y
DATA
Interviews
Examinations
Questio
Questionare
Questionares
RECORDS
CENSUS DATA

13. 13
DATA
QUALITATIVE DATA
(Discrete/ frequency data)
QUANTITATIVE DATA
(Continuous data)
Subjects with same characteristic
are counted to form specific
groups.
They have magnitude.

14. SCALES OF
DATA
MEASUREME
NT
14

15. 15
NOMINAL
SCALE
ORDINAL
SCALE
INTERVAL
SCALE
RATIO SCALE
Non
Categorical
Categorical
SCALES OF DATA
MEASUREMENT

16. NOMINAL SCALE
A nominal variable consists of named categories, with no
implied order among the categories.
"Existential" variables
16

17. ORDINAL SCALE
An ordinal scale consists of ordered categories, where the
difference between categories cannot be considered to be
equal.
17

18. INTERVAL SCALE
An interval scale has equal distances between values, but
the zero is arbitrary.
18

19. RATIO SCALE
A ratio scale has equal interval between values and a
meaningful zero point.
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20. SCALES AT GLANCE
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Scale type Assumptions
Nominal
NAMED
categories
Ordinal
Ordered
categories
Interval Equal intervals
Ratio Meaningful zero

21. EXAMPLES OF SCALES
Indicate whether the following variables are nominal, ordinal, interval or
ratio.
a) your income (assuming it's more than $0).
b) a list of the different specialities in your profession.
c) the ranking of specialities with regard to income.
d) Salman Khan was described as a "10". What type of variable was
the scale?
e) a range of motion in degrees.
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22. EXAMPLES OF SCALES
f) a score of 13 out of 17 on the Anxiety Scale.
g) staging of breast cancer as type I, II, III or IV.
h) ST depression on the ECG, measured in millimeters.
i) ST depression, measured as "1"+/- 1mm, "2"= 1 to 5mm, and "3" </=
5mm.
j) ICD-9 classifications: 0295=organic psychosis, 0296=depression and
so on.
k) diastolic blood pressure , in mm Hg.
l) pain measurement on a seven-point scale 22

23. PROPORTION AND RATE
A proportion is a type of fraction in which the numerator is
subset of the denominator.
Rate is a fraction that also has a time component.
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24. DATA REPRESENTATION
Graphing for nominal data
Graphing the ordinal data
Graphing the interval and ratio data
24

25. NOMINAL DATA
Bar graphs
Histograms
Dot plots
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26. EXAMPLE
26
COURSE NUMBER OF STUDENTS
FORENSIC 25
PSM 42
PHARMACOLOGY 8
MICROBIOLOGY 12
PATHOLOGY 13

27. 27
0
12.5
25
37.5
50
forsensic psm pharma micro patho

28. 28
0
12.5
25
37.5
50
psm forensic patho micro pharma

29. 29
0 12.5 25 37.5 50
psm
forensic
patho
micro
patho

30. ORDINAL DATA
Bar graphs
Histograms
Dot plot
30

31. INTERVAL AND RATIO DATA
Histograms
Stem and leaf plot
Frequency polygon
Cumulative frequency polygon
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32. 32

33. 33

34. 34

35. 35

36. FOR MAKING HISTOGRAMS
Rank order the data
Find the range
Choose the width
New table giving you interval midpoint count etc
Turn into histogram
Lose some info on the way
36

37. 37

38. 38

39. 39

40. 40

41. PIC OF FREQUENCY
POLYGON
41

42. CUMULATIVE FREQUENCY
POLYGON
42

43. HISTOGRAM FOR 3 GROUPS
43

44. FREQUENCY POLYGON FOR 3
GROUPS
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45. SO WHEN TO USE WHAT ?
Bar graphs and Histograms can be used for all types of
data.
But when more than 2 groups than frequency polygon.
Use graphs to show relationships not to report
numbers.
45

46. 46
0
12.5
25
37.5
50
psm forensic patho micro pharma

47. 47
42%
25%
13%
12%
8%

48. 48
0
100
200
300
400
April May June July

49. TABLE 1
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50. TABLE VERSION 2.0
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51. TABLE VERSION 3.0
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52. NUMBERS
A specific data point - the value of variable for one subject is
represented by capital letter X.
We denote the mean of a variable by putting a bar over the
capital letter X: X
The number of subjects in the sample is represented by N.
n indicates the sample size of a group.
Use subscript notation to differentiate between various sample
sizes data points etc.
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53. NUMBERS
Nominal data
Ordinal data
Ratio and interval data
53

54. 54

55. INTERVAL AND RATIO DATA
Mean is the measure of central tendency.
A measure of central tendency is the typical value for the
data.
55

56. ORDINAL DATA
Median is the measure of central tendency.
The median is that value such that half of the data points fall
above it half below it .
56

57. NOMINAL DATA
Mode is the measure of central tendency.
The mode is the most frequently occurring category.
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58. MEASURE OF DISPERSION
Refers to how closely
the data cluster
around the measure
of central tendency.
58

59. NOMINAL / ORDINAL DATA
Index of dispersion
59

60. ORDINAL DATA
Range (diff between highest and lowest values)
Interquartile range / midspread
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61. 61

62. INTERVAL OR RATIO DATA
Interquartile range
The mean deviation
The variance and the standard deviation
The coefficient of variation
62

63. Mean deviation
63

64. VARIANCE AND STANDARD
DEVIATION
64

65. COEFFICIENT OF VARIANCE
65

66. 66

67. BOX PLOTS
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68. N/C: SETTING THE SCENE
A survey of schools found that the most widely used method
to get out of going to school is "not today mom I have a
headache". Based on the survey of 2000 student it was
found to be used an average of 100 times a year with a SD
of 15. Can we determine what proportion of students use
this reason at least 115 times a year; or fewer than 70 times
a year; or between 106 and 112 times annually ?
68

69. STANDARD SCORE
69

70. NORMAL DISTRIBUTION
Assumption for statistical tests
Mean and variance independent
All natural phenomenon
Distribution of means
70

71. 71

72. 72

73. NORMAL CURVE
Mean, median, mode
Symmetrical
Approaches the x axis asymptotically
73

74. HOW MANY STUDENTS 115
TIMES ?
74

75. FEWER THAN 70 TIMES IN A
YEAR ?
75

76. BETWEEN 106 -112 TIMES ?
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77. 77

78. –Winifred Castle
“Most researchers use statistics the way a
drunkard uses a lamp-post more for support than
illumination.”
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