BIOSTATISTICS

1. BIOSTATISTICS
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2. BIOSTATISTIC
S
 The application of statistics in the medical
field is specifically termed biostatistics.
 It is a science that deals with the
development and application of the most
appropriate methods of 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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Designing Conducting Analyzing Reporting
Minimizing
biases
Confounding
factors
Measuring
random errors
Understanding
the research
Make suggestions
on hypothesis
testing &
analysis
Calculating the
sample size
Determine the
power of the
study
Ensure
continuity
throughout the
research
Assess the
statistical
significance of
the results
Efficacy & safety
of the drug
Therapy

5.  Sample size refers to the number of
participants or observations included in a
study.
 This number is usually represented by n.
 The size of a sample influences two statistical
properties:
 1) the precision of our estimates and
 2) the power of the study to draw
conclusions.
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6.  The factors affecting sample sizes are
 study design,
 method of sampling,
 outcome measures – effect size,
 standard deviation,
 study power,
 significance level
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7.  When we have data, we have to apply process analysis, then
we have to use the tests of significance to check whether the
hypothesis is true or not.
 These tests are of 2 types-
 Parametric tests
 Non Parametric tests
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8. Parametric tests
 These tests are used when the data is
quantitative and is normally distributed
(equal mean, median and mode).
 Compare means and standard deviations
 More powerful
 Ex-T-tests, Z-tests, ANOVA
Non parametric tests
 These tests are used when the data has a
skewed distribution.
 Compare percentage and proportions
 Less powerful
 Ex-Wilcoxon rank tests, chi-square tests.
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9.  A t-test is a statistical test that is used to compare
the means of two groups.
 It is often used in hypothesis testing to determine whether two
groups are different from one another.
The formula used is-
T= observed difference between two means of small samples
Standard error of difference in two small samples
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Unpaired T-tests:
•Applied on unpaired
data of independent
observations made
on individuals of
separate groups.
Paired T-test:
•Applied to paired
data of independent
observations from
one sample only.

11. ANOVA
 ANOVA is to test the differences among the means of
the population by examining the amount of variation
within each sample related to the amount of variation
between the samples
 This technique was invented by R.A Fischer and is
thus often referred to as the fischer’s ANOVA
 Types of ANOVA-
 One way ANOVA
 Two way ANOVA
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12. The ANOVA formula is given by:⇒
Where,
 F - The ANOVA coefficient
 MST - The mean sum of all the squares due to the treatment
 MSE - The mean sum of squares due to error
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13. CORRELATION
COEFFICIENTS
 Correlation coefficients are used to measure
how strong a relationship is between
two variables.
 There are several types of correlation
coefficient, but the most popular is Pearson’s.
 Pearson’s correlation (also called Pearson’s R)
is a correlation coefficient commonly used
in linear regression.
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Correlation coefficient formula

15.  Linear regression is the most widely used
statistical technique; it is a way to model a
relationship between two sets
of variables.
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16.  The Wilcoxon signed-rank test is a nonparametric
test used to compare two related samples,
matched samples, or repeated measurements on a
single sample to assess whether their population
means ranks differ.
 It is used as an alternative to the paired T-Tests.
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Wilcoxon signed-rank test

18. CHI-SQUARE TEST
 A chi-squared test (symbolically represented as χ2) is basically a data analysis on the basis of
observations of a random set of variables.
 This test was introduced by Karl Pearson in 1900
 The chi-square test is used to estimate how likely the observations that are made would be, by
considering the assumption of the null hypothesis as true.
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20.  A p-value is a statistical measurement used to validate
a hypothesis against observed data.
 A p-value measures the probability of obtaining the
observed results, assuming that the null hypothesis is
true.
 The lower the p-value, the greater the statistical
significance of the observed difference.
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21.  When you perform a statistical
test a p-value helps you determine
the significance of your results in
relation to the null hypothesis.
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