About this Course
If you’ve ever skipped over`the results section of a medical paper because terms like “confidence interval” or “p-value” go over your head, then you’re in the right place. You may be a clinical practitioner reading research articles to keep up-to-date with developments in your field or a medical student wondering how to approach your own research. Greater confidence in understanding statistical analysis and the results can benefit both working professionals and those undertaking research themselves. If you are simply interested in properly understanding the published literature or if you are embarking on conducting your own research, this course is your first step. It offers an easy entry into interpreting common statistical concepts without getting into nitty-gritty mathematical formulae. To be able to interpret and understand these concepts is the best way to start your journey into the world of clinical literature. That’s where this course comes in - so let’s get started! The course is free to enroll and take. You will be offered the option of purchasing a certificate of completion which you become eligible for, if you successfully complete the course requirements. This can be an excellent way of staying motivated! Financial Aid is also available.

100% online course

Start instantly and learn at your own schedule.

Approx. 21 hours to complete

Suggested: 6 weeks of study, 2-3 hours/week
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Subtitles: English

Skills you will gain

StatisticsClinical ResearchData AnalysisSampling StatisticsSampling (Statistics)

100% online course

Start instantly and learn at your own schedule.

Approx. 21 hours to complete

Suggested: 6 weeks of study, 2-3 hours/week
Comment Dots


Subtitles: English

Syllabus - What you will learn from this course


5 hours to complete

Getting things started by defining study types

Welcome to the first week of this course. We’ll be tackling five broad topics to provide you with an intuitive understanding of clinical research results. This isn’t a comprehensive statistics course, but it offers a practical orientation to the field of medical research and commonly used statistical analysis. The first topics will look at research methods and the collection of data - with a specific focus on study types. By the end of the lectures you should be able to identify which study types are being used and why the researchers selected them when you are reading a paper....
11 videos (Total 45 min), 11 readings, 2 quizzes
Video11 videos
About the course2m
Observing and intervening: Observational & experimental studies3m
Observing and describing: Case series studies3m
Comparing groups: Case-control studies3m
Collecting data at one point in time: Cross-sectional studies3m
Studying a group with common traits: Cohort studies4m
Let's intervene: Experimental studies6m
Working with existing research: Meta-analysis and Systematic Review4m
Doing a literature search: Part 14m
Doing a literature search: Part 26m
Reading11 readings
How this course works10m
Pre-course survey10m
Study types10m
Key notes: Observational and experimental studies10m
Key notes: Case series studies10m
Key notes: Case-control studies10m
Key notes: Cross-sectional studies10m
Key notes: Cohort studies10m
Key notes: Experimental studies10m
Key notes: Meta-analysis and systematic review10m
Peer review introduction10m
Quiz1 practice exercises
Test your knowledge: Study types16m


4 hours to complete

Describing your data

With the next topics, we finally get started with the statistics. Have you ever looked at the methods and results section of any healthcare research publication and noted the variety of statistical tests used? You would have come across terms like t-test, Mann-Whitney-U test, Wilcoxon test, Fisher’s exact test and the ubiquitous chi-squared test. Why so many tests you might wonder? It’s all about types of data. In this week, I am going to tackle the differences in data which determine what type of statistical test we can use in making sense of our data....
15 videos (Total 64 min), 12 readings, 4 quizzes
Video15 videos
Some key concepts: Definitions4m
Data types1m
Arbitary classification: Nominal categorical data1m
Natural ordering of attributes: Ordinal categorical data2m
Measurements and numbers: Numerical data types3m
How to tell the difference: Discrete and continuous variables3m
Measures of central tendency5m
Measures of dispersion6m
(Optional) Setting up spreadsheet software to do your own analysis3m
(Optional) Descriptive statistics using spreadsheet software9m
Making inferences: Sampling5m
Types of sampling3m
Case study 19m
Reading12 readings
Key notes: Definitions10m
Key notes: Data types10m
Key notes: Nominal categorical data10m
Key notes: Ordinal categorical data10m
Key notes: Numerical data types10m
Key notes: Discrete and continuous variables10m
Key notes: Describing the data10m
Key notes: Measures of central tendency10m
Key notes: Measures of dispersion10m
Visual representation of data10m
Key notes: Sampling10m
Key notes: Types of sampling10m
Quiz4 practice exercises
Test your knowledge: Data types10m
Test your knowledge: Measures of central tendency and dispersion10m
Test your knowledge: Sampling10m
Week 2 Graded Quiz20m


4 hours to complete

Building an intuitive understanding of statistical analysis

There is hardly any healthcare professional who is unfamiliar with the p-value. It is usually understood to have a watershed value of 0.05. If a research question is evaluated through the collection of data points and statistical analysis reveals a value less that 0.05, we accept this a proof that some significant difference was found, at least statistically.In reality things are a bit more complicated than that. The literature is currently full of questions about the ubiquitous p-vale and why it is not the panacea many of us have used it as. During this week you will develop an intuitive understanding of concept of a p-value. From there, I'll move on to the heart of probability theory, the Central Limit Theorem and data distribution....
14 videos (Total 78 min), 12 readings, 4 quizzes
Video14 videos
Working out the probability: Rolling dice5m
Area under the curve: Continuous data types4m
Introduction to the central limit theorem: The heart of probability theory1m
Asymmetry and peakedness: Skewness and Kurtosis4m
Learning from the lotto: Combinations4m
Approximating a bell-shaped curve: The central limit theorem4m
Patterns in the data: Distributions2m
The bell-shaped curve: Normal distribution3m
Plotting a sample statistic: Sampling distribution7m
Standard normal distribution: Z distribution9m
Estimating population parameters: t-distribution3m
(Optional) Generating random data point values using spreadsheet software6m
Case study 217m
Reading12 readings
Key notes: P-values10m
Key notes: Rolling dice10m
Key notes: Continuous data types10m
Introduction to the central limit theorem10m
Key notes: Skewness and kurtosis10m
Key notes: Combinations10m
Key notes: Central limit theorem10m
Key notes: Distributions10m
Key notes: Normal distribution10m
Key notes: Sampling distribution10m
Key notes: Z-distribution10m
Key notes: The t-distibution10m
Quiz4 practice exercises
Test your knowledge: Probability10m
Test your knowledge: The central limit theorem10m
Test your knowledge: Distributions10m
Week 3 Graded Quiz20m


4 hours to complete

The important first steps: Hypothesis testing and confidence levels

In general, a researcher has a question in mind that he or she needs an answer to. Everyone might have an opinion on the question (or answer), but an investigator looks for the answer by designing an experiment and investigating the outcome. In the first lesson we will look at hypotheses and how they relate to ethical and unbiased research and reporting.We'll also tackle Confidence intervals which I believe are one of the least understood and often misrepresented values in healthcare research. The most common tests used in the literature to compare numerical data point values are t-tests, analysis of variance, and linear regression. In the last lesson we take a closer look at these tests, but perhaps more importantly, their strict assumptions. ...
8 videos (Total 33 min), 6 readings, 3 quizzes
Video8 videos
Testing assumptions: Null and alternative hypothesis3m
Is there a difference?: Alternative Hypothesis4m
Type I and II: Hypothesis testing errors3m
Introduction to confidence intervals3m
How confident are you?: Confidence levels3m
Interval estimation: Confidence intervals3m
(Optional) Calculating confidence intervals using spreadsheet software10m
Reading6 readings
Key notes: Null and alternative hypothesis10m
Key notes: Alternative hypothesis10m
Key notes: Hypothesis errors10m
Key notes: Introduction to confidence intervals10m
Key notes: Confidence levels10m
Key notes: Confidence intervals10m
Quiz2 practice exercises
Testing your knowledge: Hypothesis10m
Test your knowledge: Confidence intervals10m


3 hours to complete

Which test should you use?

The most common statistical test that you might come across in the literature is the t-test. There are, in actual fact, a few t-tests, but the one most are familiar with, is of course, Student’s t-test and its ubiquitous p-value. Not everyone, though, knows that the name Student was actually a pseudonym, used by William Gosset (1876 - 1937). Parametric tests have very strict assumptions that must be met before their use is justified. In this lesson we take a closer look at these tests, but perhaps more importantly, their strict assumptions. Once you know these, you will be able to identify when these tests are used inappropriately....
15 videos (Total 86 min), 6 readings, 3 quizzes
Video15 videos
Student's t-test15m
Linear Regression4m
(Optional) Student's t-test in action12m
Introduction to nonparametric tests3m
Checking for normality5m
Thinking nonparametrically2m
Comparing paired observations: Signs2m
Ordering values: Ranking2m
Paired comparisons: Sign ranks2m
Summation of ranks: Rank sums6m
Comparing two populations: Mann-Whitney-U test4m
More nonparametric tests5m
Case study 313m
Reading6 readings
Key notes: Parametric tests10m
Key notes: Student's t-test10m
Key notes: ANOVA10m
Key notes: Linear regression10m
Key notes: Nonparametric tests10m
Key notes: Nonparametric tests10m
Quiz3 practice exercises
Test your knowledge: Parametric tests10m
Test your knowledge: Non-parametric tests8m
Week 5 Graded Quiz20m


2 hours to complete

Categorical data and analyzing accuracy of results

Congratulations! You've reached the final week of the course Understanding Clinical Research. In this lesson we will take a look at how good tests are at picking up the presence or absence of disease, helping us choose appropriate tests, and how to interpret positive and negative results. We’ll decipher sensitivity, specificity, positive and negative predictive values. You'll end of this course with a final exam, to test the knowledge and application you've learned in this course. I hope you've enjoyed this course and it helps your understanding of clinical research. ...
8 videos (Total 34 min), 4 readings, 3 quizzes
Video8 videos
Observed frequencies: Contingency tables5m
Comparing observed and expected values: Chi-square test3m
Association between two variables: Fisher's exact test2m
(Optional) Calculating chi-square test using spreadsheet software7m
Introduction to sensitivity and specificity2m
Measuring performance: Sensitivity and specificity4m
Proportions of results: Positive and negative predictive values6m
Reading4 readings
Key notes: Comparing categorical data10m
Keynotes: Sensitivity, specificity, positive and negative predictive values10m
Interesting online videos10m
Congratulations on completing the course10m
Quiz3 practice exercises
Testing your knowledge: Comparing categorical data12m
Test your knowledge: Sensitivity, specificity and predictive values10m
Week 6 Final examination40m
Direction Signs


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Top Reviews

By LKJul 29th 2017

Great course!! it is gonna provide you a good foundation in clinical studies if you want to start your career in clinical research! Clear explanation and comprehensive case study! highly recommended!

By DSMay 27th 2018

I'm very new at this theme, this course has being the perfect beginning. If you don't have a mathematical background and you don't understand when the funny S appear, this is the course for you!


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The University of Cape Town is the oldest university in South Africa and is one of the leading research universities on the African continent. UCT has over 25 000 students, of whom 30% are postgraduate students. We offer degrees in six faculties: Commerce, Engineering & the Built Environment, Health Sciences, Humanities, Law, and Science. We pride ourself on our diverse student body, which reflects the many cultures and backgrounds of the region. We welcome international students and are currently home to thousands of international students from over 100 countries. UCT has a tradition of academic excellence that is respected world-wide and is privileged to have more than 30 A-rated researchers on our staff, all of whom are recognised as world leaders in their field. Our aim is to ensure that our research contributes to the public good through sharing knowledge for the benefit of society. Past students include five Nobel Laureates – Max Theiler, Alan Cormack, Sir Aaron Klug, Ralph Bunche and, most recently, J M Coetzee....

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