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Introduction to
Biostatistics and
Types of Sampling
Methods
SUBMITTED BY
DR. SUNITA OJHA
ASSISTANT PROFESSOR
SURESH GYAN VIHAR UNIVERSITY
• Statistics
• Statistics is the study of the collection, organization, analysis, interpretation, and
presentation of data.
• Biostatistics
• Application in biological experiment designing, data collection, analysis and
interpretation. Biostatistics is applied in various fields such as medicine,
pharmacy, agriculture, and fisheries.
• Data
• Data in statistics are based on individual observations.
• Data can be counted or measured. It represents varying values of variable.
• Variable
• Any quantity or quality that shows variation from one individual to the other in
same population is known as variable.
• E.g. Plant height, height of adult male, weight of preschool children.
Terminologies
• Qualitative variable
• E.g. color of flower petal
• Quantitative variable
• E.g. Height of plant
• Continuous variable
• Continuous variables are those which can have any value within a
certain range exhibited by the population.
• Quantitative variables belong to this group.
• E.g. weight, height, volume, time.
• Discontinuous or Discrete variables
• Variables with certain finite values without any intermediate values
are discontinuous variables.
• All qualitative variables and some quantitative variables are discrete
• E.g. Organisms per unit area.
• Independent Variable
• The variable which is manipulated by the investigator.
• Dependent Variable
• The variable which is observed or measured.
• Control/Constant Variable
• This is the variable which the investigator wants to keep constant in
her experiment.
Water (2L)
Temp-100°C
Time- 10 mins
Water (2L)
Temp-100°C
Time- 30 mins
Water (2L)
Temp-100°C
Time- 50 mins
Independent Variable: Time
Dependent Variable: Volume of water
Control Variable: Temperature, atmospheric
pressure
Figure 1. Illustration of independent/ dependent/constant variables
• Sampling
• Selection of a part of a population to represent a whole population is known as
sampling
• Sample
• The part selected is known as sample
• Population
• The total no. of individual observation from which observations has to be made
at particular time.
• Finite: If a population consists of a fixed no. of values. E.g. Number of plants in a
quadrat
• Infinite: Population in which it is theoretically impossible to observe all the
values (unlimited size). E.g. no. of phytoplankton in a pond.
• Sampling
• Complete coverage of population is too time consuming and expensive.
Therefore a part of the population is selected that can represent the population.
Population
Sample
• A sample should possess the following essentials.
• Selected samples should be homogeneous and should not have any difference
when compared with population
• More number of items are to be included for reliable results
• The individual items in the sample should be independent of each other.
• Advantages
• Saves time.
• Reduces cost.
• Only method to study infinite population.
• Gives more accurate result.
• Shortcomings
• Sampling is difficult for small population size.
• Result can be faulty, misleading if sampling is not done properly.
• Personal biasness results faulty results.
• Sampling Size
• It is an important factor to be considered during sampling.
• It should note be too small or too large.
• The sampling size depends on the type of population.
• Homogeneous population: Small sample size is required.
• Heterogeneous population: Large sampling size is required.
• For data classification large sized sample is required.
• Methods of sampling
• There are various methods of sampling which is listed in figure no. 3. The
selection of sampling methods depends on the purpose of sampling and the
nature of population.
• Random Sampling Methods
• Random sampling
• In this method each item of the population has an equal chance of being
included in sample. This method is also known as “unrestricted random
sampling”.
Random
Sampling
Methods
Random
Sampling
Stratified
Sampling
Systematic
Sampling
Cluster
Sampling
Non-Random
Sampling
Methods
Judgmental
Sampling
Convenience
Sampling
Quota
Sampling
Lottery
Method
Random
Numbers
Sampling Methods
Figure 2. Various sampling techniques
• Lottery Method
• This is the most popular and simplest method of selecting a random sample
from a finite population.
• In this method, all items of population are numbered on separate slips of paper
of identical size, shape and color.
• These slips are folded and mixed up in a box and a blindfold selection is made.
• For required sample size, the same number of slips are selected.
• This method is in applicable for infinite population.
• Random Numbers
• This method is applied for large size population.
• The random selection is conveniently done with the help of these tables of
random numbers.
• Tippets Random Number Tables: 1040 numbers of four digits each
• Fisher and Yates Tables: 15000 numbers with two digits each
• C.R. Rao, Mitra and Matahi Table of Random Numbers: 20000 digits
grouped into 50000 sets of 4 digit number.
• Snedecor and Cochran Random Number Tables: 1000 random numbers
• One can use the table of random numbers from any position either horizontally or vertically. Or
one Can blindly start from any position of a table.
• If we want to select 10 pods from 200 pods each pod should be assigned a number from 001-
200.
• In the 5 digit Snedecor and Cochran Random Number Tables we can take three digits into
consideration and other two are ignore.
05217 03164 19774 12696 05437 17805 09609 09284 17771
• Thus the sample selected will be
• 052, 031, 197, 126, 054, 178, 096, 092, 177
• Stratified Sampling
• Stratified sampling is done when population is heterogeneous with respect to
variable or characteristic under study.
• Here population is divided into relatively homogeneous groups or strata and a
random sample is drawn from each group.
• Systematic Sampling
• For this we arrange the items in numerical, alphabetical, geographical or any
other order.
• Now the items are serially numbered. The first item is selected at random.
• E.g. If we want to select a sample of 10 trees from 100 trees of a forest by
taking every kth tree where ‘k’ refers to the sampling interval.
k= N/n
N= Population size
N=Sample size
• Here k=100/10=10. So evert 10th tree is taken as sample. i.e. 10th, 20th ,30th……
• Cluster Sampling
• Widely used in geographical studies and when the units are spread over large
geographical area.
• This area can be divide into different clusters and data are collected from these
clusters.
• Non –Random Sampling Methods
• Judgement, purposive or deliberate sampling
• In this method choice of sample items depends exclusively on the
judgement of the investigator.
• E.g. 8 out of 20 persons use a particular type of toothpaste. So the
investigator has selected those 8 person for his study.
• Convenience sampling
• Convenience sampling is a non-probability sampling technique where
subjects are selected because of their convenient accessibility and
proximity to the researcher
• Quota sampling:
• Here sample quotas are fixed according to any characteristic of the
population like income, age, sex, religion.
References
Khan, I. A., & Khanum, A. (1994). Fundamentals of biostatistics. Ukaaz.
Sharma, A. K. (2005). Text book of biostatistics I. Discovery Publishing House.
Daniel, W. W., & Cross, C. L. (2018). Biostatistics: a foundation for analysis in
the health sciences. Wiley.

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Introduction to Biostatistics and types of sampling methods

  • 1. Introduction to Biostatistics and Types of Sampling Methods SUBMITTED BY DR. SUNITA OJHA ASSISTANT PROFESSOR SURESH GYAN VIHAR UNIVERSITY
  • 2. • Statistics • Statistics is the study of the collection, organization, analysis, interpretation, and presentation of data. • Biostatistics • Application in biological experiment designing, data collection, analysis and interpretation. Biostatistics is applied in various fields such as medicine, pharmacy, agriculture, and fisheries. • Data • Data in statistics are based on individual observations. • Data can be counted or measured. It represents varying values of variable. • Variable • Any quantity or quality that shows variation from one individual to the other in same population is known as variable. • E.g. Plant height, height of adult male, weight of preschool children. Terminologies
  • 3. • Qualitative variable • E.g. color of flower petal • Quantitative variable • E.g. Height of plant • Continuous variable • Continuous variables are those which can have any value within a certain range exhibited by the population. • Quantitative variables belong to this group. • E.g. weight, height, volume, time. • Discontinuous or Discrete variables • Variables with certain finite values without any intermediate values are discontinuous variables. • All qualitative variables and some quantitative variables are discrete • E.g. Organisms per unit area.
  • 4. • Independent Variable • The variable which is manipulated by the investigator. • Dependent Variable • The variable which is observed or measured. • Control/Constant Variable • This is the variable which the investigator wants to keep constant in her experiment. Water (2L) Temp-100°C Time- 10 mins Water (2L) Temp-100°C Time- 30 mins Water (2L) Temp-100°C Time- 50 mins Independent Variable: Time Dependent Variable: Volume of water Control Variable: Temperature, atmospheric pressure Figure 1. Illustration of independent/ dependent/constant variables
  • 5. • Sampling • Selection of a part of a population to represent a whole population is known as sampling • Sample • The part selected is known as sample • Population • The total no. of individual observation from which observations has to be made at particular time. • Finite: If a population consists of a fixed no. of values. E.g. Number of plants in a quadrat • Infinite: Population in which it is theoretically impossible to observe all the values (unlimited size). E.g. no. of phytoplankton in a pond. • Sampling • Complete coverage of population is too time consuming and expensive. Therefore a part of the population is selected that can represent the population. Population Sample
  • 6. • A sample should possess the following essentials. • Selected samples should be homogeneous and should not have any difference when compared with population • More number of items are to be included for reliable results • The individual items in the sample should be independent of each other. • Advantages • Saves time. • Reduces cost. • Only method to study infinite population. • Gives more accurate result. • Shortcomings • Sampling is difficult for small population size. • Result can be faulty, misleading if sampling is not done properly. • Personal biasness results faulty results.
  • 7. • Sampling Size • It is an important factor to be considered during sampling. • It should note be too small or too large. • The sampling size depends on the type of population. • Homogeneous population: Small sample size is required. • Heterogeneous population: Large sampling size is required. • For data classification large sized sample is required. • Methods of sampling • There are various methods of sampling which is listed in figure no. 3. The selection of sampling methods depends on the purpose of sampling and the nature of population. • Random Sampling Methods • Random sampling • In this method each item of the population has an equal chance of being included in sample. This method is also known as “unrestricted random sampling”.
  • 9. • Lottery Method • This is the most popular and simplest method of selecting a random sample from a finite population. • In this method, all items of population are numbered on separate slips of paper of identical size, shape and color. • These slips are folded and mixed up in a box and a blindfold selection is made. • For required sample size, the same number of slips are selected. • This method is in applicable for infinite population. • Random Numbers • This method is applied for large size population. • The random selection is conveniently done with the help of these tables of random numbers. • Tippets Random Number Tables: 1040 numbers of four digits each • Fisher and Yates Tables: 15000 numbers with two digits each • C.R. Rao, Mitra and Matahi Table of Random Numbers: 20000 digits grouped into 50000 sets of 4 digit number. • Snedecor and Cochran Random Number Tables: 1000 random numbers
  • 10. • One can use the table of random numbers from any position either horizontally or vertically. Or one Can blindly start from any position of a table. • If we want to select 10 pods from 200 pods each pod should be assigned a number from 001- 200. • In the 5 digit Snedecor and Cochran Random Number Tables we can take three digits into consideration and other two are ignore. 05217 03164 19774 12696 05437 17805 09609 09284 17771 • Thus the sample selected will be • 052, 031, 197, 126, 054, 178, 096, 092, 177
  • 11. • Stratified Sampling • Stratified sampling is done when population is heterogeneous with respect to variable or characteristic under study. • Here population is divided into relatively homogeneous groups or strata and a random sample is drawn from each group. • Systematic Sampling • For this we arrange the items in numerical, alphabetical, geographical or any other order. • Now the items are serially numbered. The first item is selected at random. • E.g. If we want to select a sample of 10 trees from 100 trees of a forest by taking every kth tree where ‘k’ refers to the sampling interval. k= N/n N= Population size N=Sample size • Here k=100/10=10. So evert 10th tree is taken as sample. i.e. 10th, 20th ,30th……
  • 12. • Cluster Sampling • Widely used in geographical studies and when the units are spread over large geographical area. • This area can be divide into different clusters and data are collected from these clusters. • Non –Random Sampling Methods • Judgement, purposive or deliberate sampling • In this method choice of sample items depends exclusively on the judgement of the investigator. • E.g. 8 out of 20 persons use a particular type of toothpaste. So the investigator has selected those 8 person for his study. • Convenience sampling • Convenience sampling is a non-probability sampling technique where subjects are selected because of their convenient accessibility and proximity to the researcher • Quota sampling: • Here sample quotas are fixed according to any characteristic of the population like income, age, sex, religion.
  • 13. References Khan, I. A., & Khanum, A. (1994). Fundamentals of biostatistics. Ukaaz. Sharma, A. K. (2005). Text book of biostatistics I. Discovery Publishing House. Daniel, W. W., & Cross, C. L. (2018). Biostatistics: a foundation for analysis in the health sciences. Wiley.