Targeted literature searches are a fundamental part of writing clinical manuscripts that will meet the standards of high-quality journals and contribute meaningfully to evidence-based practice. When physicians write clinical manuscripts, utilizing a targeted literature search can identify high-quality, relevant, and current evidence. While a general literature review is useful, a targeted literature search is specific to the clinical question and should be completed through frameworks established, such as PICO (Population, Intervention, Comparator, Outcome) and PRISMA [1].

Biostatistics in Practice: From Data Collection to Analysis

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Biostatistics in Practice: From Data Collection to Analysis

Table of Content
1. What are Biostatistics?
2. Why is Biostatistics Important?
3. Types of Data in Biostatistics
4. Descriptive Statistics
5. Data Visualization Techniques
6. Common Statistical Terms
7. Inferential Statistics: Drawing Conclusions
8. Common Statistical Tests
9. Common Mistakes in Biostatistics
10. Conclusion

In today’s evidence-based scientific environment, biostatistics is an invaluable resource for anyone involved in medical, biological, or public health research. It doesn’t matter if you are performing an analysis of a clinical trial, studying a public health intervention, or examining data-generating use cases in genetics; biostatistics helps make sense of complicated and often unclear data.[1]

In medical research data collection techniques, and life sciences research, biostatistics ensures data accuracy and validity from the start of the study design.

1. What are Biostatistics?

Biostatistics is the application of statistical methods to biological and health-related studies. It is fundamental to the design of experiments, the collection of data, the analysis of results, and the decision-making that occurs because of data in medical, environmental, and public health arenas.[2]

2. Why is Biostatistics Important?

Biostatistics assists in: [3]

Identifying patterns in biological data
Designing valid clinical studies
Assessing treatment effectiveness
Informed public health decision making
Authors in publishing believable research – justified through data analysis.

Example: Biostatistics guides the decision on how many participants to use for testing a new drug, how to randomize, and how to tell if it really works. It also supports data collection, sample size, and medical study planning, and helps choose the data collection method for healthcare research appropriately.

3. Types of Data in Biostatistics

Correct data classification is the foundation for accurate analysis. Data in biostatistics fall into four main categories: [4]

Data Type	Description	Examples
Nominal	Categorical, no inherent order	Gender, Blood type
Ordinal	Categorical, ordered	Pain level (low, medium, high)
Interval	Numeric, equal intervals, no true zero	Temperature in Celsius
Ratio	Numeric with a true zero	Height, Weight, Age

These classifications guide both quantitative data collection methods for biomedical researchers and qualitative data collection in life sciences studies.

4. Descriptive Statistics

Descriptive statistics summarize your information to help you understand the centre, spread, and distribution of your dataset.[5]

Measures of Central Tendency	Measures of Dispersion
• Mean: Average value • Median: Middle value • Mode: Most frequent value	• Range: Difference between the highest and lowest • Standard Deviation (SD): Spread about the mean • Variance: The square of SD • Interquartile Range (IQR): Range between the 25th and 75th percentile

5. Data Visualization Techniques

Graphs and plots help identify trends, spot outliers, and communicate results clearly. Visualization is crucial when summarizing both experimental data collection in life sciences and observational data collection methods in healthcare.

Graph Type	Best for	Example
Histogram	Continuous data	Blood pressure distribution
Bar chart	Categorical data	Disease prevalence by region
Box plot	Comparing groups	Cholesterol levels in men vs women
Scatter plot	Correlation between 2 variables	Age vs Blood Sugar Levels

6. Common Statistical Terms

Term	Definition	Example
Population	The entire group under study	All diabetics in the US
Sample	Subset of the population	500 diabetic patients from hospitals
Variable	Measured attribute	Blood glucose, Age
Parameter	Summary value for population	True average BMI
Statistic	Summary from a sample	Sample mean BMI

These terms are fundamental in both primary vs secondary data in medical research and experimental studies across healthcare disciplines.

7. Inferential Statistics: Drawing Conclusions

Researchers utilize inferential statistics to make predictions or decisions about a population based on some sample data. It is central to surveys, interviews, observations in medical research, and experimental data interpretation. [6]

7.1. Testing of Hypotheses

Null Hypothesis (H₀): No effect or no difference exists [7]
Alternative Hypothesis (H₁): An effect or difference exists
p-value: Probability that the observed data occurred by chance [8]
Confidence Interval (CI): Range of the most likely values to contain the true effect. If p < 0.05 → A Significant result has been found.

8. Common Statistical Tests

Use parametric tests for normally distributed data and non-parametric tests otherwise. These tests are fundamental tools in both statistical analyses and meta-analyses.

Test	Used For	Example
t-test	Comparing two means	Drug A vs Drug B on blood pressure
Chi-square	Comparing categorical variables	Smoking status vs disease presence
ANOVA	Comparing 3+ groups	Cholesterol across age groups
Correlation (r)	Strength of the relationship	Height vs Weight
Regression	Predicting outcomes	Predicting BP from age and weight

9. Common Mistakes in Biostatistics

Misunderstanding p-values as evidence
Disregarding effect sizes
Utilizing a test that is incorrect for the measurement scale
Cherry-picking outcomes
Failing to check the normality of the data
Ignoring methods for handling missing data.

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Conclusion

Biostatistics is integral in research in the life sciences, medicine, and public health. This introductory module provided a conceptual foundation of fundamental ideas, from types of data and descriptive statistics to visualisation and basic statistical tests. It is important to know how to classify your data, how to summarise it effectively, and how to choose a relevant statistical test to produce valid, reproducible, and interpretable research.

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References

Ali, Z., & Bhaskar, S. B. (2016). Basic statistical tools in research and data analysis. Indian journal of anaesthesia, 60(9), 662–669. https://doi.org/10.4103/0019-5049.190623
Hazra, A., & Gogtay, N. (2016). Biostatistics Series Module 1: Basics of Biostatistics. Indian journal of dermatology, 61(1), 10–20. https://doi.org/10.4103/0019-5154.173988
Zapf, A., Rauch, G., & Kieser, M. (2020). Why do you need a biostatistician?. BMC medical research methodology, 20(1), 23. https://doi.org/10.1186/s12874-020-0916-4
Ranganathan, P., & Gogtay, N. J. (2019). An Introduction to Statistics – Data Types, Distributions and Summarizing Data. Indian journal of critical care medicine: peer-reviewed, official publication of Indian Society of Critical Care Medicine, 23(Suppl 2), S169–S170. https://doi.org/10.5005/jp-journals-10071-23198
Kaliyadan, F., & Kulkarni, V. (2019). Types of Variables, Descriptive Statistics, and Sample Size. Indian dermatology online journal, 10(1), 82–86. https://doi.org/10.4103/idoj.IDOJ_468_18
Guetterman T. C. (2019). Basics of statistics for primary care research. Family medicine and community health, 7(2), e000067. https://doi.org/10.1136/fmch-2018-000067
Pernet C. (2015). Null hypothesis significance testing: a short tutorial. F1000Research, 4, 621. https://doi.org/10.12688/f1000research.6963.3
Andrade C. (2019). The PValue and Statistical Significance: Misunderstandings, Explanations, Challenges, and Alternatives. Indian journal of psychological medicine, 41(3), 210–215. https://doi.org/10.4103/IJPSYM.IJPSYM_193_19