This document provides an introduction to biostatistics and its key concepts. Biostatistics applies statistical analysis to biological and medical data. It is used to 1) establish differences between groups, 2) validate methods, and 3) determine incidence and prevalence of diseases. Statistics can be descriptive, summarizing data through measures of central tendency and dispersion, or inferential, using probability to make generalizations about populations from samples. Common statistical tests include t-tests, z-tests, chi-squared tests, and F-tests. Parameters represent population values that are estimated through sample statistics.

1. 1
Introduction to biostatistics
Statistics deals with any type of data.
Statistics may be defined as science and art of collection,
organization, analysis and interpretation of data on the basis of
which certain decisions may be taken.
Biostatistics: It is the knowledge of statistics which is applied
to biological science. Biostatistics is virtually a medical statistics.
Vital Statistics deals with data relating to the vital events of
human being e.g. birth, death etc.
Importance of statistics
1. Statistical knowledge is required to establish the
significance of difference between two groups
2. To establish validity of any method.

2. Biostatistics-2
3. Example: Laparoscopic surgery is better than general
surgery. It may be proved if statistical data shows that in
laparoscopic surgery
 Chances of infection is less
 Duration of hospital stoppage is less
 Use of antibiotic is less
4. To know the incidence and the prevalence rate of any
disease.
5. To know the vital events of human being such as birth,
death, marriage etc.
6. To define the health level in the community.
Divisions of Statistics
a. Descriptive Statistics
 The statistical procedures used in describing the important
characteristics or properties of a set of data derived from
samples are often referred to as descriptive statistics.
 It is a procedure or technique used to organize and
summarize numerical data into a frequency distribution,
computing of measures of central tendency (e.g., means.
median and mode) and measures of dispersion (e.g.,
quartile deviation, mean deviation and standard deviation
etc).
 Sometimes these types of studies are called hypothesis
generating studies (to contrast them with hypothesis
testing studies).
b. Inferential Statistics
[Q: Write short notes on: a) Inferential
statistics(BSMMU, July, 2010, January 2009)]
 The procedures used and applied to sample, in drawing of
inferences about the properties of population from sample
data are called inferential statistics.
 Basically, inferential statistics utilize the mathematics of
probability theory to infer or induce generalization about

3. Biostatistics-3
populations from sample data. The most commonly used
inferential statistics is t test, z-test, x2 test and F tests.
Common statistical symbols/notations
N (n) = total number of
subject
Σ = Summation
μ = population mean
d.f. = degree of freedom
CL= confidence limit.
P = probability.
x = variable
y = another variable
x = means of x variable
y =means of y variable
Parameter
[Q. Write shorts notes on: Parameter. (BSMMU, July,
2010)]
[Parameter: A parameter is a numerical quantity measuring some aspect of a
population of scores. For example, the mean is a measure of central tendency.
Greek letters are used to designate parameters. Below shown several
parameters of great importance in statistical analyses and the
Greek symbol that represents each one. Parameters are rarely
known and are usually estimated by statistics computed in
samples. To the right of each Greek symbol is the symbol for
the associated statistic used to estimate it from a sample.
Quantity Parameter Statistic
Mean μ M
Standard deviation σ s
Proportion π p
Correlation ρ r

4. Biostatistics-10