Improving Your Statistical Questions

Go to Course: https://www.coursera.org/learn/improving-statistical-questions

Introduction

### Course Review: Improving Your Statistical Questions on Coursera In the realm of data science, empirical research plays a vital role in drawing meaningful conclusions and making informed decisions. However, the foundation of any successful research endeavor largely depends on the rigor of the questions asked. The Coursera course **"Improving Your Statistical Questions"** serves as an essential guide for researchers who wish to enhance the quality and relevance of their statistical inquiries. #### Course Overview The course emphasizes the importance of asking better statistical questions to drive empirical investigation. It encourages participants to reflect on conventional norms and rethink how research can be more informative and interesting. With a blend of theoretical insights, practical assignments, and thought-provoking discussions, this course enriches the learner’s ability to conceptualize and reformulate statistical questions, making them more impactful in their research practice. #### Syllabus Breakdown **Module 1: Improving Your Statistical Questions** This module kicks off with the fundamentals of formulating clear and precise statistical questions. Participants explore various types of questions and the nuances of hypothesis testing. By stressing the importance of understanding what one truly wants to know through a study, this section lays a strong groundwork for the rest of the course. **Module 2: Falsifying Predictions** Making predictions without the possibility of being wrong is deemed futile. Here, learners will gain insights into creating falsifiable predictions, which is crucial for credible research. The module provides practical strategies for setting boundaries around predicted ranges and understanding the implications of effect sizes. **Module 3: Designing Informative Studies** A well-designed study is essential to obtaining meaningful results. This module directs participants toward justifying error rates and utilizing the concept of the smallest effect size of interest in power analyses. Moreover, it introduces simulation as a tool, which can be incredibly beneficial for refining one’s statistical comprehension and research design. **Module 4: Meta-Analysis and Bias Detection** In a landscape often riddled with biases, assessing literature can be daunting. This module tackles publication and selection biases, equipping learners with meta-analytical techniques essential for understanding and interpreting the scientific literature critically. **Module 5: Computational Reproducibility, Philosophy of Science, and Scientific Integrity** Integrity in research is non-negotiable. This final content-rich module addresses the importance of reproducibility in data analysis and how one's philosophical stance influences the types of research questions posed. It also delves into the complexities of scientific integrity and how to align research practices with the highest standards of reliability. **Module 6: Final Exam** Culminating in a graded final exam, this module tests the thorough understanding of concepts covered throughout the course. It serves as a solid benchmark for evaluating one’s readiness to apply learned skills in real-world research contexts. #### Recommendations **Who Should Enroll?** This course is ideal for researchers, data analysts, and students who have a foundational understanding of statistics and wish to refine their research methodologies. Whether you are in academia, industry, or just an avid learner, the insights offered are invaluable for enhancing your analytical capabilities. **Why Take This Course?** 1. **Hands-On Approach:** Practical assignments help in translating theoretical concepts into actionable research strategies. 2. **Expert Instruction:** The course is guided by knowledgeable instructors who provide real-world perspectives and champion best practices in research. 3. **Improved Research Results:** By focusing on the quality of questions, participants will learn how to yield more reliable and informative results, ultimately elevating the standard of their work. In conclusion, **"Improving Your Statistical Questions"** is a course worth considering for anyone committed to advancing their empirical research capabilities. The structured modules and emphasis on practical application make it a highly recommended resource for fostering a deeper understanding of statistical inquiry. Whether you're aiming for better research outcomes or simply wish to enhance your statistical questioning skills, this course provides the foundational tools needed for achieving those goals. Sign up today and take the first step towards more rigorous and meaningful research!

Syllabus

Module 1: Improving Your Statistical Questions

One of the biggest improvements most researchers can make is to more clearly specify their statistical questions. When you perform a study, what is it you really want to know? What are different types of questions we can ask? Which question does a hypothesis test really answer, and is this answer actually what you are interested in, or is the question you are asking more about exploration, description, or prediction? How can we make riskier predictions than null-hypothesis tests, and why is this useful?

There is little use in making predictions if you can never be wrong - so how do we make sure your predictions are falsifiable? We discuss why falsifiable predictions are important, and how to make your predictions falsifiable in practice. One important aspect of making predictions falsifiable is to specify a range of values that is not predicted, and we will examine different approaches to specifying a smallest effect size of interest.

If studies are designed to answer a question, you should make sure the answer you will get after collecting data is informative. Instead of mindlessly setting Type 1 and Type 2 error rates, we will learn why it is important to be able to justify error rates, and some approaches how to do so. We discuss the benefits of using your smallest effect size of interest in power analyses, and why learning to simulate data is a useful tool. Simulations can help you to improve your understanding of statistics, enable you to design informative studies, and even ask novel questions.

Regrettably we work in a scientific enterprise where the published literature does not reflect real research. Publication bias and selection biases lead to a scientific literature that can’t be interpreted without taking these biases into account. We will discuss what real research lines look like, and how to meta-analytically evaluate the literature while keeping bias in mind.

We discuss three last topics. First, we will make sure other people can use your data to ask new questions, by making sure your data analysis is computationally reproducible. Then, we will reflect on how your philosophy of science influences the types of questions you will ask, and what you value as you do research. Finally, we discuss scientific integrity, and reflect on why our research practice is not always aligned with the best possible ways to provide reliable answers to scientific questions.

This module contains a graded exam. It covers content from the entire course. We recommend making this exam only after you went through all the other modules.