Biostatistics A–Z: Descriptive, Inferential, & Data Analysis

Name: Biostatistics A–Z: Descriptive, Inferential, & Data Analysis
Rating: 4.6 (68 reviews)

Core Biostatistics for Medicine, Public Health, and Clinical Research, Analyze & Interpret Medical Data with Confidence.

Created byArbab Majeed

Last updated 4/2026

English

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What you'll learn

Understand the fundamental role of biostatistics in medical research and healthcare decision-making
Classify and work with different types of data, variables, and levels of measurement
Differentiate between populations and samples, and understand basic sampling techniques
Summarize data using descriptive statistics such as mean, median, mode, variance, and standard deviation
Interpret data distributions, including skewness and kurtosis, and recognize patterns in datasets
Visualize data effectively using frequency distributions and histograms
Apply basic probability concepts in medical and clinical contexts
Understand key statistical distributions, including normal, binomial, and Poisson distributions
Explain the concept of sampling distributions and the central limit theorem
Perform and interpret hypothesis testing in research studies
Distinguish between Type I and Type II errors and understand statistical power
Calculate and interpret confidence intervals and p-values
Apply parametric tests such as t-tests and one-way ANOVA for comparing groups
Analyze relationships between variables using correlation and simple linear regression
Perform chi-square tests for categorical data analysis
Recognize when to use non-parametric tests as alternatives to parametric methods
Critically read and interpret statistical results in medical literature
Make informed, evidence-based decisions using statistical reasoning

Course content

6 sections • 24 lectures • 1h 47m total length

The Role of Biostatistics in Medical Research5:15
Biostatistics provides the mathematical framework to handle uncertainty in medical research, enabling evidence-based practice by distinguishing genuine treatment effects from chance throughout study design and interpretation.
Classification of Variables: Qualitative vs. Quantitative3:46
Learn how to classify variables in biostatistics as qualitative (categorical) or quantitative (numerical) and distinguish discrete from continuous data to guide appropriate statistical tests.
Population, Samples, and Sampling Frameworks4:06
Levels of Measurement: Nominal, Ordinal, Interval, and Ratio6:37

Measures of Central Tendency: Mean, Median, and Mode5:00
Measures of Variability: Range, Variance, and Standard Deviation4:49
Understanding Distribution Shape: Skewness and Kurtosis4:46
Understand how skewness and kurtosis describe distribution shape and normality, guiding whether to report the mean or median and when to apply log transformations or nonparametric tests.
Visualizing Data: Frequency Distributions and Histograms4:34
Transform raw biostatistics data into patterns using frequency distributions, grouped frequency distributions, and histograms; identify outliers, skewness, and potential bimodal groups by choosing appropriate bins.

Fundamental Principles of Probability in Medicine4:19
explore how medical decisions rely on probability, defining events and sample spaces, and applying addition and multiplication rules, independence and dependence, and conditional probability to diagnostic tests and disease risk.
Characteristics and Logic of the Normal Distribution5:14
Explore the normal distribution, a symmetric bell curve defined by mean and standard deviation, with the empirical rule (68-95-99.7) guiding medical interpretations and parametric tests like t-tests and ANOVA.
Discrete Probability: Binomial and Poisson Distributions4:01
Explore discrete probability with binomial and Poisson distributions, comparing fixed-trial two-outcome scenarios to event-rate counts over time.
Sampling Distributions and the Central Limit Theorem4:36

The Framework of Statistical Hypothesis Testing4:22
Explore the framework of statistical hypothesis testing in biostatistics, comparing the null hypothesis with the alternative, computing a test statistic, and applying a 0.05 significance level to decide evidence strength.
Understanding Type I Errors, Type II Errors, and Power4:02
Learn how alpha and beta define type I and II errors in clinical trials, and how increasing sample size boosts power to detect real effects.
Principles of Point Estimation and Confidence Intervals4:14
Interpreting P-Values and Statistical Significance4:30

The One-Sample t-Test and Z-Test Frameworks3:44
Learn how one-sample tests compare a small group's mean to a known standard using z and t frameworks, test statistics, and p-values. Emphasizes random sampling and normality assumptions.
Independent Samples t-Test for Group Comparisons3:58
Paired t-Test for Related Medical Samples3:55
Use paired t-test on change scores from the same patient or matched pairs in before-and-after studies to test whether the average change is zero, via the T statistic and p-value.
Fundamentals of One-Way Analysis of Variance (ANOVA)4:29
Learn how one-way ANOVA compares three or more groups at once, assesses whether group means differ via the F statistic, and uses post hoc tests to identify where differences lie.

Principles of Pearson and Spearman Correlation3:57
Learn how Pearson and Spearman correlations measure the strength and direction of associations between patient variables, distinguishing linear from non-linear and ordinal data, without implying causation.
Theoretical Basis of Simple Linear Regression5:16
Explore how simple linear regression uses ordinary least squares to predict outcomes from predictors, assess residuals, and test linearity, normal residuals, homoscedasticity, and independence, with R squared as the measure.
Chi-Square Tests for Independence and Goodness of Fit3:56
Apply chi-square tests to categorical data, including goodness-of-fit. Assess independence and relationships by comparing observed and expected counts, noting rules about independence and when to use Fisher's exact test.
Overview of Non-Parametric Statistical Alternatives4:00
Explore non-parametric statistical alternatives that do not assume normal distribution, ideal for small samples and ordinal data. Learn when and how to apply Mann-Whitney, Wilcoxon, and Kruskal-Wallis tests.

Requirements

Interest in medical research, healthcare, or data analysis
A willingness to learn and apply statistical concepts step by step

Description

It's an Unofficial Course.

This comprehensive Biostatistics course is designed to equip learners with the essential statistical knowledge and practical skills needed to understand, analyze, and interpret data in medical and health research. Whether you are a student, healthcare professional, or aspiring researcher, this course provides a clear and structured pathway to mastering the core principles of biostatistics without unnecessary complexity or overwhelming mathematics.

The course begins by building a strong foundation in the role of biostatistics within modern medicine and research. You will develop a clear understanding of different types of variables, how data is classified, and how populations and samples are defined and selected. These fundamental concepts are essential for anyone who wants to critically evaluate research studies or conduct their own investigations.

As you progress, you will learn how to summarize and present data effectively using descriptive statistics. Key concepts such as mean, median, mode, variability, and distribution shape are explained in a practical and intuitive way. You will also gain the ability to visualize data through frequency distributions and histograms, allowing you to identify patterns and trends that are crucial in healthcare decision-making.

The course then introduces probability and theoretical distributions, which form the backbone of statistical reasoning in medicine. You will explore how probability is applied in clinical contexts, understand the properties of the normal distribution, and learn about important discrete distributions such as binomial and Poisson. The concept of sampling distributions and the central limit theorem will help you understand how conclusions can be drawn from sample data.

Moving into inferential statistics, you will gain a deep understanding of hypothesis testing and how statistical decisions are made in research. Important topics such as Type I and Type II errors, statistical power, confidence intervals, and p-values are explained with clarity, enabling you to interpret research findings with confidence and accuracy.

The course also covers key parametric tests used to compare groups, including t-tests and analysis of variance (ANOVA). You will learn when and how to apply these tests in real-world medical scenarios, helping you evaluate differences between treatments, populations, or clinical outcomes.

In addition, you will explore the relationships between variables through correlation and regression analysis. The course explains both Pearson and Spearman correlation methods, as well as the fundamentals of simple linear regression. You will also learn how to analyze categorical data using chi-square tests and gain an overview of non-parametric alternatives for situations where traditional assumptions are not met.

By the end of this course, you will be able to understand and interpret statistical results in medical literature, perform basic statistical analyses, and make informed, data-driven decisions in healthcare and research settings. The course emphasizes clarity, practical application, and real-world relevance, making complex statistical concepts accessible and meaningful.

This course is ideal for medical students, public health learners, researchers, and healthcare professionals who want to build a solid foundation in biostatistics and confidently apply statistical thinking in their academic or professional work.

Thank you

Who this course is for:

Medical, nursing, pharmacy, and allied health students who need a strong foundation in biostatistics
Public health students and professionals involved in research and data analysis
Healthcare professionals who want to better understand and interpret medical research
Researchers and aspiring researchers looking to strengthen their statistical skills
Beginners with little or no background in statistics who want a clear and practical introduction
Anyone interested in learning how data is analyzed and used in healthcare and clinical decision-making

Biostatistics A–Z: Descriptive, Inferential, & Data Analysis

What you'll learn

Explore related topics

Course content

Foundations of Biostatistics4 lectures • 20min

Descriptive Statistics4 lectures • 19min

Probability and Theoretical Distributions4 lectures • 18min

Fundamentals of Inferential Statistics4 lectures • 17min

Parametric Tests for Comparing Groups4 lectures • 16min

Correlation, Regression, and Categorical Data4 lectures • 17min

Requirements

Description

Who this course is for: