Understanding Clinical Research: Behind the Statistics Course Reviews

The course Understanding Clinical Research: Behind the Statistics authored by Juan H. Klopper and available on Coursera, provides a comprehensive overview of the fundamental concepts and principles behind clinical research, focusing on the interpretation and significance of statistical analysis.

Understanding Clinical Research: Behind the Statistics Course Reviews
Understanding Clinical Research: Behind the Statistics Course Reviews

The course is designed to equip learners with a solid understanding of the methodologies and techniques used in clinical research, enabling them to critically evaluate and interpret clinical studies.

The course covers various aspects, including the design and planning of clinical trials, ethical considerations, data collection and management, and the role of statistical analysis in drawing meaningful conclusions from research findings. Learners delve into the intricacies of study designs, such as randomized controlled trials, observational studies, and systematic reviews, learning about their strengths, limitations, and appropriate applications.

The course also emphasizes the importance of statistical literacy for healthcare professionals and researchers. Participants learn about key statistical concepts, such as hypothesis testing, p-values, confidence intervals, and effect sizes. Through practical examples and case studies, learners gain insights into how to interpret statistical results and assess the clinical relevance of research outcomes.

Moreover, the course explores challenges related to bias, confounding variables, and sources of error that can impact research outcomes. Learners are encouraged to develop a critical mindset when evaluating research methodologies and results, fostering a deeper understanding of the nuances involved in clinical research interpretation.

By the end of the course, participants should have a solid foundation in understanding clinical research methodologies, statistical analysis, and the factors influencing the validity and reliability of research findings. This knowledge equips them with the skills to contribute meaningfully to evidence-based decision-making in the field of healthcare and clinical research.


Course Content:

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.

The course Understanding Clinical Research: Behind the Statistics by Juan H. Klopper on Coursera is divided into 6 modules. Here is a detailed breakdown of the modules:

Module 1: Getting things started by defining study types

Welcome to the first week. Here we’ll provide an intuitive understanding of clinical research results. So this isn’t a comprehensive statistics course - rather it offers a practical orientation to the field of medical research and commonly used statistical analysis. The first topics we will look at are research methods and data collection with a specific focus on study types. By the end, you should be able to identify which study types are being used and why the researchers selected them, when you are later reading a published paper.

What's included

11 videos  11 readings  1 quiz

11 videosTotal 45 minutes
  • Introduction to Understanding Clinical Research2 minutesPreview module
  • About the course2 minutes
  • Observing and intervening: Observational & experimental studies3 minutes
  • Observing and describing: Case series studies3 minutes
  • Comparing groups: Case-control studies3 minutes
  • Collecting data at one point in time: Cross-sectional studies3 minutes
  • Studying a group with common traits: Cohort studies4 minutes
  • Let's intervene: Experimental studies6 minutes
  • Working with existing research: Meta-analysis and Systematic Review4 minutes
  • Doing a literature search: Part 14 minutes
  • Doing a literature search: Part 26 minutes
11 readingsTotal 100 minutes
  • How this course works5 minutes
  • Pre-course survey5 minutes
  • Study types10 minutes
  • Key notes: Observational and experimental studies10 minutes
  • Key notes: Case series studies10 minutes
  • Key notes: Case-control studies10 minutes
  • Key notes: Cross-sectional studies10 minutes
  • Key notes: Cohort studies10 minutes
  • Key notes: Experimental studies10 minutes
  • Key notes: Meta-analysis and systematic review10 minutes
  • Peer review introduction10 minutes
1 quizTotal 30 minutes
  • Test your knowledge: Study types30 minutes


Module 2: Describing your data

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. This week I am going to tackle the differences in data that determine what type of statistical test we can use in making sense of our data.

What's included

15 videos  12 readings  4 quizzes

15 videosTotal 64 minutes
  • Introduction2 minutesPreview module
  • Some key concepts: Definitions4 minutes
  • Data types1 minute
  • Arbitary classification: Nominal categorical data1 minute
  • Natural ordering of attributes: Ordinal categorical data2 minutes
  • Measurements and numbers: Numerical data types3 minutes
  • How to tell the difference: Discrete and continuous variables3 minutes
  • Introduction1 minute
  • Measures of central tendency5 minutes
  • Measures of dispersion6 minutes
  • (Optional) Setting up spreadsheets to do your own analysis3 minutes
  • (Optional) Descriptive statistics using spreadsheets9 minutes
  • Making inferences: Sampling5 minutes
  • Types of sampling3 minutes
  • Case study 19 minutes
12 readingsTotal 120 minutes
  • Key notes: Definitions10 minutes
  • Key notes: Data types10 minutes
  • Key notes: Nominal categorical data10 minutes
  • Key notes: Ordinal categorical data10 minutes
  • Key notes: Numerical data types10 minutes
  • Key notes: Discrete and continuous variables10 minutes
  • Key notes: Describing the data10 minutes
  • Key notes: Measures of central tendency10 minutes
  • Key notes: Measures of dispersion10 minutes
  • Visual representation of data10 minutes
  • Key notes: Sampling10 minutes
  • Key notes: Types of sampling10 minutes
4 quizzesTotal 120 minutes
  • Week 2 Graded Quiz30 minutes
  • Test your knowledge: Data types30 minutes
  • Test your knowledge: Measures of central tendency and dispersion30 minutes
  • Test your knowledge: Sampling30 minutes


Module 3: 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.

What's included

14 videos  12 readings  4 quizzes

14 videosTotal 78 minutes
  • P-values: P is for probability1 minutePreview module
  • Working out the probability: Rolling dice5 minutes
  • Area under the curve: Continuous data types4 minutes
  • Introduction to the central limit theorem: The heart of probability theory1 minute
  • Asymmetry and peakedness: Skewness and Kurtosis4 minutes
  • Learning from the lotto: Combinations4 minutes
  • Approximating a bell-shaped curve: The central limit theorem4 minutes
  • Patterns in the data: Distributions2 minutes
  • The bell-shaped curve: Normal distribution3 minutes
  • Plotting a sample statistic: Sampling distribution7 minutes
  • Standard normal distribution: Z distribution9 minutes
  • Estimating population parameters: t-distribution3 minutes
  • (Optional) Generating random data point values using spreadsheet software6 minutes
  • Case study 217 minutes
12 readingsTotal 120 minutes
  • Key notes: P-values10 minutes
  • Key notes: Rolling dice10 minutes
  • Key notes: Continuous data types10 minutes
  • Introduction to the central limit theorem10 minutes
  • Key notes: Skewness and kurtosis10 minutes
  • Key notes: Combinations10 minutes
  • Key notes: Central limit theorem10 minutes
  • Key notes: Distributions10 minutes
  • Key notes: Normal distribution10 minutes
  • Key notes: Sampling distribution10 minutes
  • Key notes: Z-distribution10 minutes
  • Key notes: The t-distibution10 minutes
4 quizzesTotal 120 minutes
  • Week 3 Graded Quiz30 minutes
  • Test your knowledge: Probability30 minutes
  • Test your knowledge: The central limit theorem30 minutes
  • Test your knowledge: Distributions30 minutes


Module 4: The important first steps: Hypothesis testing and confidence levels

In general, a researcher has a question in mind that he or she needs to answer. Everyone might have an opinion on this question (or answer), but a researcher looks for the answer by designing an experiment and investigating the outcome. First, 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.

What's included

8 videos  6 readings  2 quizzes

8 videosTotal 32 minutes
  • Introduction to Hypothesis Testing1 minutePreview module
  • Testing assumptions: Null and alternative hypothesis3 minutes
  • Is there a difference?: Alternative Hypothesis4 minutes
  • Type I and II: Hypothesis testing errors3 minutes
  • Introduction to confidence intervals3 minutes
  • How confident are you?: Confidence levels3 minutes
  • Interval estimation: Confidence intervals3 minutes
  • (Optional) Calculating confidence intervals using spreadsheet software10 minutes
6 readingsTotal 60 minutes
  • Key notes: Null and alternative hypothesis10 minutes
  • Key notes: Alternative hypothesis10 minutes
  • Key notes: Hypothesis errors10 minutes
  • Key notes: Introduction to confidence intervals10 minutes
  • Key notes: Confidence levels10 minutes
  • Key notes: Confidence intervals10 minutes
2 quizzesTotal 60 minutes
  • Testing your knowledge: Hypothesis30 minutes
  • Test your knowledge: Confidence intervals30 minutes


Module 5: 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.

What's included

15 videos  6 readings  3 quizzes

15 videosTotal 85 minutes
  • Introduction to parametric tests2 minutesPreview module
  • Student's t-test15 minutes
  • ANOVA4 minutes
  • Linear Regression4 minutes
  • (Optional) Student's t-test in action12 minutes
  • Introduction to nonparametric tests3 minutes
  • Checking for normality5 minutes
  • Thinking nonparametrically2 minutes
  • Comparing paired observations: Signs2 minutes
  • Ordering values: Ranking2 minutes
  • Paired comparisons: Sign ranks2 minutes
  • Summation of ranks: Rank sums6 minutes
  • Comparing two populations: Mann-Whitney-U test4 minutes
  • More nonparametric tests5 minutes
  • Case study 313 minutes
6 readingsTotal 60 minutes
  • Key notes: Parametric tests10 minutes
  • Key notes: Student's t-test10 minutes
  • Key notes: ANOVA10 minutes
  • Key notes: Linear regression10 minutes
  • Key notes: Nonparametric tests10 minutes
  • Key notes: Nonparametric tests10 minutes
3 quizzesTotal 90 minutes
  • Week 5 Graded Quiz30 minutes
  • Test your knowledge: Parametric tests30 minutes
  • Test your knowledge: Non-parametric tests30 minutes


Module 6: 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.

What's included

13 videos  4 readings  4 quizzes

13 videosTotal 56 minutes
  • Introduction to comparing categorical data2 minutesPreview module
  • Observed frequencies: Contingency tables5 minutes
  • Comparing observed and expected values: Chi-square test3 minutes
  • Association between two variables: Fisher's exact test2 minutes
  • (Optional) Calculating chi-square test using spreadsheet software7 minutes
  • Introduction to sensitivity and specificity2 minutes
  • Measuring performance: Sensitivity and specificity4 minutes
  • Proportions of results: Positive and negative predictive values6 minutes
  • Introdution to risk and odds ratios0 minutes
  • Risk and odds ratios - Losses (Risk)6 minutes
  • Risk and odds ratios - Losses (Odds)6 minutes
  • Risk and odds ratios - Wins4 minutes
  • Risk and odds ratios example4 minutes
4 readingsTotal 40 minutes
  • Key notes: Comparing categorical data10 minutes
  • Keynotes: Sensitivity, specificity, positive and negative predictive values10 minutes
  • Congratulations on completing the course10 minutes
  • Risk and odds ratios10 minutes
4 quizzesTotal 90 minutes
  • Week 6 Final examination30 minutes
  • Honors: Risk and odds ratios0 minutes
  • Testing your knowledge: Comparing categorical data30 minutes
  • Test your knowledge: Sensitivity, specificity and predictive values30 minutes



As a former participant of the Understanding Clinical Research: Behind the Statistics course by Juan H. Klopper on Coursera, I am pleased to provide an evaluation of the course based on my experience.

This course proved to be an enlightening journey into the realm of clinical research and its intricate connection to statistical analysis. Dr. Klopper's expertise in the field became evident from the very start. His clear and concise explanations effectively demystified complex concepts, making them accessible to learners with varying levels of prior knowledge.

One of the standout features of the course was its well-structured curriculum. The progression from fundamental concepts to more advanced topics was seamless, allowing participants to build a strong foundation before delving into more nuanced areas. The inclusion of real-world case studies and practical examples brought a tangible dimension to the theoretical content, making it easier to grasp the practical implications of statistical findings.

The emphasis on critical evaluation was a hallmark of the course. Through assignments and discussions, I honed my ability to dissect research methodologies, identify potential biases, and assess the validity of statistical results. This skill has proven invaluable not only in my understanding of research papers but also in my day-to-day interactions with medical literature.

Dr. Klopper's teaching style fostered an engaging and interactive learning environment. His passion for the subject matter shone through, keeping me consistently motivated to explore each module. Moreover, the sense of community within the online platform, facilitated by active discussions and peer interactions, added a collaborative dimension to the learning experience.

The course's practical relevance cannot be understated. The knowledge gained has empowered me to critically evaluate medical studies, understand the nuances of statistical significance, and engage in evidence-based decision-making. The course's application in real-world medical scenarios has been evident, and I find myself better equipped to contribute meaningfully to discussions with colleagues and healthcare professionals.

In conclusion, Understanding Clinical Research: Behind the Statistics is a standout course for anyone seeking to bridge the gap between clinical research and statistical analysis. Dr. Klopper's expertise, the comprehensive curriculum, and the emphasis on critical thinking make it a transformative educational experience. The skills acquired extend beyond the course itself, impacting one's ability to navigate the dynamic landscape of medical research with confidence and insight.


What you'll learn:

Upon completing the course Understanding Clinical Research: Behind the Statistics by Juan H. Klopper on Coursera, participants will acquire a range of valuable skills:

  1. Critical Evaluation: Learners will develop the ability to critically evaluate clinical research studies, assessing the validity of study designs, methodologies, and statistical analyses. This skill is essential for making informed decisions about the applicability and reliability of research findings.

  2. Statistical Literacy: Participants will gain a solid understanding of fundamental statistical concepts, such as p-values, confidence intervals, effect sizes, and hypothesis testing. This statistical literacy empowers them to interpret and communicate the significance of research results accurately.

  3. Study Design Knowledge: Learners will become familiar with various types of study designs, including randomized controlled trials, observational studies, and systematic reviews. This knowledge enables them to understand the strengths and limitations of different approaches to clinical research.

  4. Data Interpretation: Through practical examples and case studies, participants will learn how to interpret statistical outcomes in a clinical context. This skill enables them to draw meaningful conclusions from research data and consider the clinical implications of findings.

  5. Ethical Awareness: The course covers ethical considerations in clinical research, helping learners understand the importance of ethical guidelines and regulations in conducting research involving human subjects.

  6. Research Collaboration: Participants will be better equipped to collaborate effectively with researchers and healthcare professionals, as they will share a common understanding of research methodologies and statistical concepts.

  7. Evidence-Based Decision Making: Armed with a deeper understanding of clinical research, learners can contribute more effectively to evidence-based decision-making in healthcare settings, ensuring that decisions are grounded in sound research findings.

  8. Bias and Confounding Recognition: The course highlights challenges related to bias, confounding variables, and sources of error in research. Participants will develop the ability to recognize these issues and consider their impact on research results.

  9. Communication Skills: Learners will enhance their ability to communicate research findings and their implications to both technical and non-technical audiences, bridging the gap between researchers, healthcare professionals, and the public.

  10. Continuous Learning: The course fosters a mindset of continuous learning in the field of clinical research, encouraging participants to stay updated with new methodologies, advancements, and best practices.

Overall, completing this course equips participants with the knowledge and skills needed to engage with clinical research critically, contribute to evidence-based healthcare practices, and make informed decisions based on a solid understanding of statistical analysis and research methodologies.



Dr. Juan H. Klopper is a prominent figure in the field of clinical research and medicine. With a distinguished career spanning both academia and clinical practice, he has made significant contributions to the understanding and advancement of healthcare through research and education.

Dr. Klopper is a seasoned medical professional who has demonstrated expertise in multiple areas of clinical medicine, research methodology, and medical education. He has a background in endocrinology and thyroid disorders, with a specific focus on thyroid cancer. His extensive experience in patient care, coupled with his dedication to research, has allowed him to bridge the gap between theoretical knowledge and practical application in the medical field.

As an educator, Dr. Klopper is known for his ability to convey complex medical concepts in an accessible and engaging manner. He has been involved in various educational initiatives, including online courses like "Understanding Clinical Research: Behind the Statistics" on Coursera. Through his teaching, he empowers learners to understand the intricacies of clinical research methodologies, statistical analysis, and critical evaluation of research findings.

Dr. Klopper's scholarly contributions include research papers, articles, and presentations on topics ranging from clinical trial design to thyroid cancer management. His work reflects a dedication to evidence-based medicine and a commitment to improving patient outcomes through rigorous research and informed decision-making.

Given his background, experience, and commitment to advancing healthcare knowledge, Dr. Juan H. Klopper is widely regarded as a reputable expert in the field of clinical research and medical education. His multidisciplinary expertise, ability to simplify complex concepts, and dedication to enhancing research literacy make him a valuable asset to both the medical community and individuals seeking to deepen their understanding of clinical research and statistics.



The course Understanding Clinical Research: Behind the Statistics authored by Juan H. Klopper, typically includes the following requirements:

  1. Interest in Clinical Research: Participants should have an interest in clinical research methodologies, statistical analysis, and their application in healthcare and medical decision-making.

  2. Basic Medical Knowledge: While the course does not usually require an extensive medical background, a basic understanding of medical terminology and concepts can be beneficial for comprehending the content.

  3. Access to Online Learning Platform: As the course is often hosted on platforms like Coursera, participants need access to a computer, tablet, or smartphone with an internet connection to participate in lectures, discussions, and assignments.

  4. English Proficiency: Since the course materials and instruction are in English, a reasonable level of proficiency in English reading, writing, and listening is important.

  5. Commitment to Learning: Like any educational endeavor, the course demands a commitment of time and effort. Participants should be prepared to engage with course materials, complete assignments, and actively participate in discussions.

  6. Open-Mindedness: The course covers a range of topics related to clinical research, statistical analysis, and medical decision-making. An open-minded approach to learning and a willingness to explore new concepts are beneficial.

  7. Critical Thinking Skills: The course encourages critical evaluation of research methodologies and statistical results. Participants should possess or be willing to develop critical thinking skills to assess the validity and significance of research findings.

  8. Computer Literacy: Basic computer skills, such as navigating online platforms, participating in online discussions, and submitting assignments, are essential for engaging with the course content.

  9. Time Management: The course may have a structured schedule with assignments and deadlines. Participants should manage their time effectively to complete the course requirements within the specified time frame.

  10. Desire to Apply Learning: The course is designed to equip learners with practical skills to interpret clinical research and statistical results. A willingness to apply the knowledge gained to real-world scenarios in healthcare is valuable.

These requirements collectively contribute to a meaningful and engaging learning experience in the Understanding Clinical Research: Behind the Statistics course, enabling participants to enhance their understanding of clinical research methodologies and their role in medical decision-making.


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