The document outlines the basic statistical methods used in biomedical research, emphasizing data collection, organization, cleaning, and analysis with both qualitative and quantitative data types. It discusses hypothesis testing, the use of various statistical tests such as t-tests and ANOVA, and the interpretation of results through confidence intervals and p-values. Additionally, it details how to present statistical findings in research manuscripts, utilizing SPSS for analysis.

1. Basic Statistical Analysis for
Bio Medical Research
Dr. Swati Patel

2. Data Collection
Explaratory Data Analysis
Data Organization
(Master chart preparation)
Data Cleaning
Hypothesis testing
Inferential Statistics
Conclusion & Interpretation
Descriptive Statistic

4. Types of Medical Data

5. • Qualitative Data: Qualitative data helps in categorizing and understanding
the demographic and social characteristics of patients.
• Qualitative data are descriptive and non-numeric, such as patient gender,
ethnicity, or medical history.
• Quantitative Data: Quantitative data provides precise measurements and
allows for statistical analysis to assess trends, associations, and outcomes.
• Quantitative data are numerical and can be measured and analyzed
statistically. They include variables like blood pressure, cholesterol levels,
and patient age.

6. • Quantitative data can be further categorized into continuous data (e.g., blood
pressure, height) and discrete data (e.g., number of hospital visits, counts of
specific medical events).
• Continuous data can take any value within a certain range and can be
measured with great precision. Continuous data can theoretically have an
infinite number of values within a range, making them suitable for statistical
analysis using techniques like mean, standard deviation, and correlation.
• Discrete data are whole numbers or counts that cannot be subdivided into
smaller units.

7. Qualitative data:
• Categorical data: values belong to categories
Nominal data: there is no natural order to the categories
e.g. blood groups
Ordinal data: there is natural order e.g. Adverse Events
(Mild/Moderate/Severe/Life Threatening)
•Binary data: there are only two possible categories
e.g. alive/dead

8. The researcher calculates
summary statistics to describe the
characteristics of the data.
TThe researcher uses inferential
statistics to make predictions or draw
conclusions about a population
based on sample data

9. Basic steps to select the statistical test
Type of Variables
Qualitative Variable
(Binary/Categorical)
Quantitative Variable
(Numerical)
2 groups > 2 groups
Independent Dependent
Independent
t-test
Paired t-test
ANOVA Repeated measures of
ANOVA
Independent Dependent
2 binary/ordinal/
Nominal Variables
2 independent
population
proportion
Independent Dependent
Chi-Square test
Fisher Exact test
Mc Nemar test
Z-test of
Proportion

10. Data Analysis using SPSS
• Independent t-test
• Paired t-test
• ANOVA
• Chi-Square test
• Fisher Exact test
• Mc Nemar`s test
• Z- test of Proportion

11. 95 % Confidence Interval
• A 95% confidence interval (CI) provides a range of values within
which we are 95% confident that the true population parameter
lies.
• This means that if we repeatedly sample from the population
and compute the estimation parameter that will be lies in
between the value of 95% Confidence interval only.
• Suppose we are estimating the average systolic blood pressure
in a population, and we compute a 95% confidence interval of
[120, 130] mmHg based on a sample of individuals.
• This means that we are 95% confident that the true average
systolic blood pressure in the population falls somewhere

12. P- Value
• The p-value, or probability value, is a measure used in
statistical hypothesis testing to determine the strength of
evidence against the null hypothesis.
• Suppose a researcher is conducting a study to determine if a new medication is effective in
reducing pain. They collect data from a sample of patients and perform a statistical test.
• The resulting p-value is 0.02.

13. Independent t-test out come table
Study Variables N Mean Std. Deviation P-Value
Haemoglobin Case 60 10.50 1.834
Control 30 13.68 1.234
ESR Case 60 42.32 19.008
Control 30 14.67 4.310
Calprotectin Case 60 818.02 345.755
Control 30 35.21 14.392
D.dimer Case 60 766.42 286.347
Control 30 185.54 98.582

15. Paired t test

16. Chi-Square test

21. How to write the Statistical Analysis in Manuscript / Dissertation
The present study included Qualitative as well as quantitative variables. Qualitative
variables were summarised by frequency (%) whereas quantitative variables were
summarised as mean and SD or Median(IQR).
An Independent t-test was applied to compare the two qualitative variables and
ANOVA was applied to compare more than qualitative variables considering the
95% level of significance using SPSS 20 software.

22. Thank you