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.
