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 TendencyMeasures 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

  1. 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
  2. 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
  3. 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
  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
  5. 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
  6. 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
  7. Pernet C. (2015). Null hypothesis significance testing: a short tutorial. F1000Research, 4, 621. https://doi.org/10.12688/f1000research.6963.3
  8. 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