Biostatistics is the application of statistics to biology and medicine. It involves the collection, organization, analysis, interpretation and presentation of data. The goals of biostatistics are to guide study design, analyze results using appropriate statistical methods, and interpret and present findings to researchers. It uses terminology such as population, sample, probability, and p-values. Data can be presented numerically in tables, graphically using charts and plots, or mathematically using summary statistics and measures of central tendency and dispersion.

1. Introduction toIntroduction to
BiostatisticsBiostatistics
Mr. NIRAJ KUMAR YADAV
Lecturer of Biostatistics
G S Medical College & Hospital
Pilkhuwa, Hapur U.P
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2. IntroductionIntroduction
Statistics- The science of collecting,
summarizing and analyzing numerical data.
Biostatistics- Sometimes known as biometrics,
most generally, is the application of statistics to
biology and, most commonly, to medicine.
The application of statistics to a wide range of
topics in biology.
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3. BiostatisticsBiostatistics
It is the science which deals with
development and application of the most
appropriate methods for the:
Collection of data.
Presentation of the collected data.
Analysis and interpretation of the results.
Making decisions on the basis of such
analysis
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4. Other definitions for “Statistics”
 Frequently used in referral to recorded data
 Denotes characteristics calculated for a set
of data : sample mean
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5. Role of statisticians
 To guide the design of an experiment or
survey prior to data collection
 To analyze data using proper statistical
procedures and techniques
 To present and interpret the results to
researchers and other decision makers
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6. Terminology use in
Biostatistics
 PopulationPopulation-- A population is a set of similarA population is a set of similar
items or events which is of interest for someitems or events which is of interest for some
question or experiment.question or experiment.
 Sample-Sample- A data sample is a set of dataA data sample is a set of data
collected and/or selected from a statisticalcollected and/or selected from a statistical
population by a defined procedure.population by a defined procedure.
 Probability-Probability- Probability is a mathematicalProbability is a mathematical
technique for predicting outcomes. It predictstechnique for predicting outcomes. It predicts
how likely it is that specific event will occur.how likely it is that specific event will occur.
It is measured on a scale from 0 to 1.It is measured on a scale from 0 to 1.
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7.  P-values-P-values- TheThe pp-value or probability value-value or probability value
is the probability for a given statisticalis the probability for a given statistical
model that, when the null hypothesis ismodel that, when the null hypothesis is
true.true.
 DependentDependent variable-variable- The dependentThe dependent
variables represent the output or outcomevariables represent the output or outcome
whose variation is being studied.whose variation is being studied.
 IndependentIndependent variable-variable- An independentAn independent
variable is the variable you have controlvariable is the variable you have control
over, what you can choose and manipulateover, what you can choose and manipulate
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8. Sources of
data
Records Surveys Experiments
Comprehensive Sample
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9. Types of dataTypes of data
Constan
t Variable
s
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10. Quantitative
continuous
Types of variables
Quantitative variables Qualitative variables
Quantitative
descrete
Qualitative
nominal
Qualitative
ordinal
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12. Examples:
Ratio DataRatio DataRatio DataRatio Data
Interval
Data
Interval
Data
Ordinal
Data
Ordinal
Data
Nominal
Data
Nominal
Data
Level of Measurement and
Measurement scales
Ordered categories
(Ranking, Order or
scaling)
Difference between
measurements but no
true zero
Difference between
measurements, true
zero exists
Categories (no
ordering
or direction)
Height, Age, Weekly
Food Spending
Temperature in
Fahrenheit,
Standardized exam
score
Service Quality rating,
Standard & poor’s bond
rating, Student letter
grades
Marital Status, Eyes
Color, Type of car
owned
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13.  Numerical presentationNumerical presentation
 Graphical presentationGraphical presentation
 Mathematical presentationMathematical presentation
Methods of presentation of data
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14. 1- Numerical presentation
Tabular presentation (simple – complex)
Name of variableName of variable
(Units of variable)(Units of variable)
FrequencyFrequency %%
--
- Categories- Categories
--
TotalTotal
Simple frequency distribution Table (S.F.D.T.)
Title
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15. Table (I): Distribution of 50 patients at the
surgical department of Alexandria hospital in
May 2008 according to their ABO blood
groups
BloodBlood
groupgroup
FrequencyFrequency %%
AA
BB
ABAB
OO
1212
1818
55
1515
2424
3636
1010
3030
TotalTotal 5050 100100
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16. Table (II): Distribution of 50 patients at the
surgical department of Alexandria hospital in
May 2008 according to their age
AgeAge
(years)(years)
FrequencyFrequency %%
20-<3020-<30
30-30-
40-40-
50+50+
1212
1818
55
1515
2424
3636
1010
3030
TotalTotal 5050 100100
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17. Complex frequency distribution Table
Table (III): Distribution of 20 lung cancer patients at the
chest department of Alexandria hospital and 40 controls in
May 2008 according to smoking
Smoking
Lung cancer
Total
Cases Control
No. % No. % No. %
Smoker 15 75% 8 20% 23 38.33
Non
smoker
5 25% 32 80% 37 61.67
Total 20 100 40 100 60 100
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18. 2- Graphical presentation
 Graphs drawn using Cartesian
coordinates
• Line graph
• Frequency polygon
• Frequency curve
• Histogram
• Bar graph
• Scatter plot
 Pie chart
 Statistical maps
rules
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19. Line Graph
0
10
20
30
40
50
60
1960 1970 1980 1990 2000
Year
MMR/1000
Year MMR
1960 50
1970 45
1980 26
1990 15
2000 12
Figure (1): Maternal mortality rate of
(country), 1960-2000
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20. Frequency polygon
Age
(years)
Sex Mid-point of
intervalMales Female
s
20 - 3 (12%) 2 (10%) (20+30) / 2 = 25
30 - 9 (36%) 6 (30%) (30+40) / 2 = 35
40- 7 (8%) 5 (25%) (40+50) / 2 = 45
50 - 4 (16%) 3 (15%) (50+60) / 2 = 55
60 - 70 2 (8%) 4 (20%) (60+70) / 2 = 65
Total 25(100%) 20(100%)
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21. Frequency polygon Age
Sex
M-P
M F
20- (12%) (10%) 25
30- (36%) (30%) 35
40- (8%) (25%) 45
50- (16%) (15%) 55
60-70 (8%) (20%) 65
0
5
10
15
20
25
30
35
40
25 35 45 55 65
Age
%
Males Females
Figure (2): Distribution of 45 patients at (place)
, in (time) by age and sex
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22. 0
1
2
3
4
5
6
7
8
9
20- 30- 40- 50- 60-69
Age in years
Frequency
Female
Male
Frequency curve
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23. HistogramHistogram
0
5
10
15
20
25
30
35
0
25
30
40
45
60
65
Age (years)
%
Figure (2): Distribution of 100 cholera patients at
(place) , in (time) by age
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24. Bar chart
0
5
10
15
20
25
30
35
40
45
%
Single Married Divorced Widowed
Marital status
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Bar chart- A bar chart or bar graph is a chart or graph that
presents grouped data with rectangular bars with lengths
proportional to the values that they represent. The bars can
be plotted vertically or horizontally. A vertical bar chart is
sometimes called a Line graph.
Bar chart-
Single variable

25. Bar Chart- Double Variable
0
10
20
30
40
50
%
Single Married Divorced Widowed
Marital status
Male
Female
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26. Pie chart
Deletion
3%
Inversion
18%
Translocation
79%
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Pie chart- A pie chart (or a circle chart) is a circular
statistical graphic which is divided into slices to illustrate
numerical proportion.

27. 3-Mathematical presentation
Summery statistics
Measures of locationMeasures of location
1- Measures of central tendency1- Measures of central tendency
2- Measures of non central2- Measures of non central
locationslocations
(Quartiles, Percentiles )(Quartiles, Percentiles )
Measures of dispersionMeasures of dispersion
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28. 1- Measures of central tendency
(averages)
Mid-rangeMid-range
Smallest observation + Largest
observation
2
ModeMode
the value which occurs with the
greatest frequency i.e. the most
common valuee
Summery statistics
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29. 1- Measures of central tendency (cont.)
 MedianMedian
the observation which lies in thethe observation which lies in the
middle of the ordered observation.middle of the ordered observation.
 Arithmetic mean (mean)Arithmetic mean (mean)
Sum of all observationsSum of all observations
Number of observationsNumber of observations
Summery statistics
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30. Measures of dispersion
 RangeRange
 VarianceVariance
 Standard déviationStandard déviation
 Semi-interquartile rangeSemi-interquartile range
 Coefficient of variationCoefficient of variation
 ““Standard error”Standard error”
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31. Standard déviation SD
7 7
7 7 7
7
7
8
7 7 7
6
3 2
7 8
13
9Mean = 7
SD=0
Mean = 7
SD=0.63
Mean = 7
SD=4.04
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32. Standard error of mean SE
SE (Mean) =SE (Mean) =
SS
nn
A measure of variability among means of
samples selected from certain population
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