This document provides details of a biostatistics lesson for dental students, including: - The date, duration, topic, objectives and evaluation method of the lesson - A lesson plan outline covering introduction to biostatistics, methods of data presentation, sampling techniques, sampling error and references - Details of the content covered on methods of data presentation including tabulation, charts, diagrams and examples - An explanation of different sampling techniques including simple random sampling, systematic sampling, stratified sampling and cluster sampling - A discussion of sampling error and non-sampling error - References cited The document aims to teach dental students about methods of data presentation and different sampling techniques in biostatistics.

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2. (part-1)
BIOSTATISTICS
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3.  Date-21/07/2016
 Topic –Biostatistics (part-1)
 Duration – 40 minutes
 Subject – Public Health Dentistry
 Time Of Class –3.00-4.00pm
 Target audience –final year B.D.S students
 Method of presentation – PowerPoint presentation
 Audio & Visual Aids Used – LCD Projector &
Laptop
 Objectives- By the end of the class, students should
understand the method of data presentation and
types of sampling,
 Method of evaluation- by asking questions at the
end of the class 9/2/20163

4.  Lesson plan
 Topic: Biostatistics part-1
 Year: B.D.S VI year
 Date: 21/07/2016
 Time: 3:00 pm -4:00 pm
 Setting:
 Aim: to teach about the method of data presentation and types of
sampling.
 Learning objectives: The students should learn about the method of
data presentation and types of samplingSl . No. CONTENT TL Methods TL Media Time
1.
Introduction
Lecture
Power point
10 mins
2.
-Presentation of data
. Lecture
Power point 20 mins
3.
Sampling techniques
.
Lecture
Power point
20 mins
4.
Sampling Error
Lecture
Power point 15 mins.
5.
•Non Sampling Discussion Power point
10mins9/2/20164

5. Contents:-
 Introduction
 Presentation of data
- Methods of presentation Data.
• Sampling techniques
• Sampling Error
• Non Sampling error
• References
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6. INTRODUCTION
The word “Statistics” is derived from Latin for ‘state’
indicating historical importance of data gathering, which
principally is demographic information. Statistics is the
science, which deals with collection, organization,
summarization, analysis, interpretation and presentation of
data.
Inferences derived from these findings help in making valid
decisions. Statistical methods and techniques applied to
biological problems or data is called Biostatistics.
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7. Presentation of Data
 What is Data?
-facts and statistics collected together for reference or
analysis.
 Data collected from various experiments.
 It should be compiled, classified and presented in a
purposive manner to bring out important points.
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8. Methods of Presentation of Data.
 Based on the data type, representation of data also differs.
 There are two different types of data in statistics; they are;
(i) discrete, and
(ii) continuous type of data.
 Discrete data are distinct and separate and also
invariably whole numbers. eg. Number of deaths due to
particular diseases.
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9.  Continuous data are those, which takes the value
between range of values, eg height, weight, age.
 There are two methods of presenting the data:-
(i) Tabulation
(ii) Charts and diagrams
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10.  Tabulation (frequency distribution table):-
The distribution of the total no. of observation among
the various categories is termed as a frequency
distribution.
Frequency
distribution
table
Discrete
data
Continuous
data
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12.  Charts and diagrams:-
Presenting data in these forms is useful in simplifying the
presentation and enhancing comprehension of the data.
Representation of data in these form provides the
following:-
They simplify the complexity.
They facilitate visual comparison of data.
They arouse the interest in reader.
They save time and labour.
They draw some conclusion directly or
indirectly.
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13. Charts and
diagrams for:-
Discrete data
1. Bar charts
2. Pie charts
3. Pictogram
Continuous data
1.Histogram
2. Line charts
3. Frequency curve
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14. 1. Bar chart:-
 These are the way of presenting a set of numbers by length
of a bar; the length of the bar is proportional to the
magnitude to be represented.
 Bar charts are easy to prepare, easy to understand and
enable visual comparison.
 There are three types of bar charts:-
 Simple bar chart
 Multiple bar chart
 Component bar charts
FOR DISCRETE
DATA
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Component bar graph showing male and female ratio

18. 2. Pie chart:-
 In these diagrams the areas of segments of a circle are
compared.
 The area of each segment depends upon the percentage,
which is converted to angle and drawn.
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Pie chart

20. 3. Pictogram:-
 These diagrams are used for a layman those who cannot
understand technical charts like bar charts.
 Here pictures and symbols are used to present the data.
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22. 1. Histogram:-
 Histogram is a set of vertical bars whose areas are
proportional to the frequencies represented.
 The class intervals are given along the horizontal
axis and the frequencies along the vertical axis.
FOR CONTINUOUS
DATA
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24. 2. Line charts:-
 It shows trends or changes in data varying with a constant,
at even intervals.
 A line chart emphasizes the flow of a constant and rate of
change, rather than amount of change.
 When we need to show trends or changes in data at uneven
or clustered intervals, an XY (scatter) chart is used.
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26. 3. Frequency curve:-
 A frequency polygon is a graphical display of a frequency
table.
 The intervals are shown on the x-axis and the number of the
scores in each interval is represented by the height of a point
located above the middle of the interval.
 The points are connected so that together with the X-axis
they form a polygon.
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28. SAMPLING TECHNIQUES
 The study population is too large and it may be too
expensive or too time consuming to attempt either a
complete or nearly complete coverage in a statistical
study, so we take a sample from the population.
 Sample is the representative of the population and to
ensure that we chose each unit of the sample
technically. This process is called sampling technique.
 Sufficient sample size is calculated based on the
proportion of and precision required. 9/2/201628

29. • Simple random sampling
• Systematic random sampling
• Stratified random sampling
• Cluster random sampling
RANDOM
SAMPLING
TECHNIQUE
• Judgment sampling
• Convenience sampling
• Quota sampling
• Purposive sampling
NON-
RANDOM
SAMPLING
TECHNIQUE
There are two methods in sampling
techniques:-
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30. Simple random sampling
 It is applicable when:-
- The population is small.
- The population is available
- the population is homogenous.
• This is done either by using random table or lottery method.
The principle used to select the sample is each and every
unit will have equal chance of getting selected.
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31. Systematic sampling
 This technique is applicable when:-
- The population is large and scattered but the population list
available (sampling frame), and
- The population is not homogeneous.
• The principle used in selecting the sample is every kth unit
of population is selected, where K is sampling interval,
which is calculated as:
Sample interval (k)= Total population/sample
size 9/2/201631

32.  This technique leads to more accurate result if the
population is homogenous.
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33. Stratified sampling
 This sampling technique is applicable when:-
- The population is large.
- The population is not homogenous.
• First the population is divided into homogenous group called
strata, and the sample is drawn from each stratum at random in
proportion to its size.
• This gives greater accuracy result.
• The demerit of this technique is, dividing the population into
homogenous group.
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35. Cluster sampling.
 Cluster sampling is applicable when preparing the
sampling frame is difficult.
 In it, geographical area is divided into small area called
cluster.
 This technique allows only small number of target
population to be sampled. Normally 30 clusters are
selected by systematic sampling method.
 Error will be more but cost of study is reduced.
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37. Judgment
 Choosing the sample items depends on the judgment
of the investigator.
 Samples are because the investigator believes that they
are typical or representative of the population under
his/her study.
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38. Convenience sampling
 Selection is made from an available source like that from a
nearly college students to study the awareness regarding
AIDS in college students, because getting sample is
convenient.
 Non- random sampling is biased and unsatisfactory, but
time, cost and resource required will be considerably less.
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40. Quota Sampling
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 The general composition of the sample is decided in
advance. The only requirement is that to find right
number of people to somehow fill the quotas.
 This is generally done to insure the inclusion of a
particular segment of the population.

41. Purposive Sampling
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 A purposive sample is a non-representative subset of some
larger population, and is constructed to serve a very
specific need or purpose..
 A subset of a purposive sample is a snowball sample
(chain referral sampling)— so named because one picks up
the sample along the way, analogous to snow ball
accumulating. snow.
 A snowball samples is achieved by asking a participant to
suggest some one else who might be willing or appropriate
for the study.

42. SAMPLING ERROR
 The occurrence of variation from one sample to
another sample is called sampling error.
 The factors that influence the sampling error
are:
- Size of the sample.
- Natural variability of the individual reading.
• As the size of the sample increases, sampling
error will decrease.
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43. NON-SAMPLING ERROR
 Sampling error is not only error which arises in a
sample survey, error may also occur due to
inadequately calibrated instruments.
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44. REFERENCES
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 Hiremath SS. Textbook of preventive and community
dentistry. 2nd edition, 2011. Elseviers Publications.
 Peter S. Essentials Of Preventive And Community
Dentistry.5TH nd Edition,2014. Arya publications.

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