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MODULE 01
INTRODUCTION TO STATISTICS FOR HEALTH
SCIENCES
Please note: This course was designed to be interacted and engaged with
using the online modules. This Module Companion Guide is a resource
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HSCI 190 INTRODUCTION TO STATISTICS FOR HEALTH SCIENCES
TABLE OF CONTENTS
- INTRODUCTION
- Land Acknowledgement
- Course Introduction
- Video: Introduction to Module
- Module 01 Learning Outcomes
- Module 01 Assessments
- Module Homework
- Module Quiz...................................................................................................................................................
- Assignment 1: Observing & Summarizing Data from the World Around Us..........................................
- Final Exam
- Course Icons
- Module Outline..................................................................................................................................................
- SECTION 01: Introduction to Statistics in Health Sciences
- What is Statistics?
- Common Misconceptions About Statistics...................................................................................................
- Video: The Math Person Myth & Negative Stereotypes..............................................................................
- Fighting the Math Person Myth
- Question: Where Do You See Statistics?.......................................................................................................
- Statistics Canada
- Statistics in Biomedical Research & Drug Trials
- Big Data, Machine Learning, & Electronic Medical Records
- Evidence-Based Medicine...............................................................................................................................
- The Power of (Mis)Information
- Forensic Statistics
- Question: Statistical Evidence in a Criminal Case........................................................................................
- Dr. Shipman’s Public Inquiry Data
- Demographics & Timeline of Dr. Shipman’s Patients
- Takeaways: Interpreting Dr. Shipman’s Public Inquiry Data
- Time of Death Comparison of Dr. Shipman’s Patients
- Drawing Conclusions from Statistics
- A Framework for Understanding Statistics: PPDAC.....................................................................................
- Question: Problems Worth Investigating
- Section 01: Summary
- SECTION 02: Levels of Measurement & Central Tendency.............................................................................
- Describing Data
- Types of Data
- Activity: Levels of Measurement....................................................................................................................
- Levels of Measurement - Key Takeaways.....................................................................................................
- Describing Data with Tables
- Categorical Data
- Scale Data.....................................................................................................................................................
- Absolute vs. Relative Frequency Distributions
- Question: Describing Data with Tables vs. Numbers..................................................................................
- Mathematical Notations
- Measures of Central Tendency
- Question: Calculating the Average Birth Weight (Mean)
- Question: Calculating the Average Birth Weight (Median)
- Question: Calculating the Average Birth Weight (Mode)
- Feedback: Birth Weight and Measures of Central Tendency
- Comparing Mean, Median, & Mode
- Pros & Cons of the Mean, Median, & Mode
- How to Choose a Measure of Central Tendency
- Question: Differences in Mean, Median, & Mode
- Section 02: Summary
- SECTION 03: Describing Variability
- Introduction to Describing Variability
- Range & Interquartile Range..........................................................................................................................
- Calculating Interquartile Range
- Describing and Interpreting Interquartile Range
- Question: Calculating IQR...............................................................................................................................
- Calculating Spread
- Problem with Calculating Spread
- Variance (s^2 )
- Problems with Variance..................................................................................................................................
- Standard Deviation
- Reporting Standard Deviation
- Question: Calculating Standard Deviation
- Utilizing Standard Deviation in Healthcare
- Section 03: Summary
- SECTION 04: Data Framing
- Data Framing & the Importance of Context
- Understanding Measurements......................................................................................................................
- Measuring COVID-19 Deaths
- Video: Data Framing & COVID-19 Deaths.....................................................................................................
- Why Measurement Matters
- Using Relative vs. Absolute Numbers
- Exaggerated Findings..................................................................................................................................
- Minimized Findings
- Comparing Answers to Questions.................................................................................................................
- Section 04: Summary
- SECTION 05: Application Using Statistical Software
- Using Statistical Tools
- Statistical Software Package Options............................................................................................................
- Module Sample Problems
- Sample Problem #1.........................................................................................................................................
- Video: Sample Problem #1
- Sample Problem #2.........................................................................................................................................
- Video: Sample Problem #2
- Section 05: Summary
- CONCLUSION
- Module 01: Conclusion
- Module Outline................................................................................................................................................
- Credits
INTRODUCTION Please see the online learning module for the full experience of interactions within this document. LAND ACKNOWLEDGEMENT This content was retrieved from Introduction Slide 1of 7 of the online learning module. Welcome to HSCI 190, Introduction to Statistics for Health Sciences! To begin let us acknowledge that Queen’s University is situated on traditional Anishinaabe and Haudenosaunee territory. To acknowledge this traditional territory is to recognize its longer history, one predating the establishment of the earliest European colonies. It is also to acknowledge this territory’s significance for the Indigenous Peoples who lived, and continue to live, upon it and whose practices and spiritualities were tied to the land and continue to develop in relationship to the territory and its other inhabitants today. We are grateful to have been able to prepare your course materials and virtual learning space on this traditional land. We also recognize that measurement and statistics is not without colonial contradictions. Unfortunately, we live in a world where “data” are too often misrepresented or fabricated to protect and serve colonial privilege. With this reflection, let us use caution and awareness in this course and beyond, and vow to always reflect for whom, by whom, and about whom data is collected. COURSE INTRODUCTION This content was retrieved from Introduction Slide 2 of 7 of the online learning module. The aim of this course is to help you build foundational knowledge on why and how statistical concepts and techniques are used/misused in the world around us. This course does not focus on memorizing formulas, rather, it focuses on building connections between course material and everyday life, and using real data to help you develop practical skills for how to apply statistics and communicate results appropriately. VIDEO: INTRODUCTION TO MODULE 01 This content was retrieved from Introduction Slide 3 of 7 of the online learning module. In Module 01, you will learn about why statistics are important, common misconceptions about statistics, and how statistics are used in Health Sciences. You will be introduced to levels of measurement, measures of central tendency (mean, median, and mode), variance, and data framing. Watch the video for an introduction to Module 01 from your instructor, Dr. Natalie Wagner. (1:43) Page Link:
You will complete six quizzes throughout the course; one quiz at the end of each module. The quizzes will consist of multiple choice and short answer questions designed to test your comprehension of module content. For more details about the module quizzes, visit the assessment page in your online learning environment. ASSIGNMENT 1: OBSERVING & SUMMARIZING DATA FROM THE WORLD AROUND US Subpage of Introduction Slide 5 of 7 – Assignment 1: Observing & Summarizing Data from the World Around Us 1/ In this assignment, you will be asked to create a data set based on your observations of a real-life phenomenon. You will be asked to calculate descriptive statistics and visualize your data set using statistical software and write a one-page summary of your data collection, statistical methods, and findings. All statistical output should be included as an appendix. For more details about this assignment, visit the assessment page in your online learning environment. FINAL EXAM Subpage of Introduction Slide 5 of 7 – Final Exam 1/ Throughout the course, all modules and assignments build on each other. Material in the modules, homework questions, and multimedia content are testable material unless otherwise noted. The final exam will be comprised of multiple choice and short answer questions. COURSE ICONS This content was retrieved from Introduction Slide 6 of 7 of the online learning module. As you navigate the HSCI 190 modules, watch for these course icons. Learn about its function in the course. Listen Up! This icon indicates the presence of an audio clip on the slide from your instructor or other content experts. To play the audio clip, click the play button. Full transcripts and closed captions are available. Calculator This icon lives in the sidebar of your module. Clicking this icon will reveal relevant equations. Additional Information
Clicking this icon will present additional facts, information, and resources to aid in your studying and recall of the material. This content will not be tested. Reference This icon lives in the sidebar of the slide. Clicking it will reveal the references for content and/or images on the slide. MODULE OUTLINE This content was retrieved from Introduction Slide 7 of 7 of the online learning module. Section 01: Introduction to Statistics in Health Sciences – Refer to page 9 Section 02: Levels of Measurement & Central Tendency – Refer to page 26 Section 03: Describing Variability – Refer to page 46 Section 04: Data Framing – Refer to page 58 Section 05: Application Using Statistical Software – Refer to page 65 End of Introduction
Reference: Son & Stigler. Introduction to Statistics: A Modelling Approach. Part 1: Exploring Variation. University of California, Los Angeles CourseKata.Org. Retrieved September 2020, from: https://coursekata.org/preview/version/0ea12f05-dce8-4b7c-a6ad-5950ec4a57bb/lesson/2/ COMMON MISCONCEPTIONS ABOUT STATISTICS This content was retrieved from Section 01 Slide 3 of 21 of the online learning module. Many people have preconceived notions about statistics. In this course you will learn in depth about what statistics is and is not, barriers to statistics, and how to apply statistical knowledge to real world scenarios. Learn about common views and misconceptions about statistics. “Statistics is all about calculations” While statistics does involve numbers and formulas, modern statistical software and computer programs can do a lot of the calculating for you. The main point of statistics is to use problem solving to decide how to analyze data and interpret results appropriately. You will learn more about the data investigation cycle and how to use statistical software throughout Module 01. “There is one right answer” In statistics, there are sets of rules and assumptions you have to learn; however, there are often many different approaches you can take to solving a problem and presenting the findings. Throughout the course, you will learn about the “art” of statistics and trade-offs between different statistical approaches. “I’m not a math person, so I won’t be able to do statistics” Math ability is a skill, not a trait. Sometimes math may feel challenging but that is part of the learning process. Learning any new skill takes practice. On the next slide, you will learn more about the “Math Person Myth” and why society needs to stop thinking this way. VIDEO: THE MATH PERSON MYTH & NEGATIVE STEREOTYPES This content was retrieved from Section 01 Slide 4 of 21 of the online learning module. There is mounting evidence disputing the claim that some people are “math people” and others are not. In fact, vocalizing that you “are not a math person” feeds into unhelpful stereotypes that negatively impact your own ability to learn, as well as the ability of those around you. Watch the TEDx talk by Dr. Nate Brown on The Math People Myth to learn more about negative stereotypes that plague statistics (12:22). Note: the details of this video will not be tested but are provided to set the tone for the course. Page Link:
https://www.youtube.com/embed/uJsHoS01pPg?start=8&end= References: Burkley, P., Parker, J., Stermer, P, and Burkey, E. (2010). Trait beliefs that make women vulnerable to math disengagement. Personality and Individual Differences, 48(2), 234-238. Retrieved September 2020, from: https://proxy.queensu.ca/login?url=https://doi.org/10.1016/j.paid.2009.09. Osborne, J. (2001). Testing Stereotype Threat: Does Anxiety Explain Race and Sex Differences in Achievement? Contemporary Educational Psychology, 26(3), 291-310. Retrieved September 2020, from: https://proxy.queensu.ca/login?url=https://doi.org/10.1006/ceps.2000. FIGHTING THE MATH PERSON MYTH This content was retrieved from Section 01 Slide 5 of 21 of the online learning module. Having an open and honest conversation about stereotypes is not easy, and the TEDx talk by Dr. Nate Brown might have brought up some uncomfortable feelings. Thankfully, there are strategies that can prevent negative stereotypes about math ability from impacting your ability to succeed in this course and beyond. Listen to Dr. Wagner discuss strategies for success in this course. Note: the details of this audio will not be tested but have been provided to ensure you set yourself up to be successful in this course. The Math Person Myth & Growth Mindset in Statistics (3:02) Start of Audio Transcript: I really, truly believe, that everyone can do statistics. This course is for anyone, from any background, and everyone can be successful. So, I’d like to ask you to throw out the notion that some people “are math people and others are not”; and if you hear someone else saying that inform them that the evidence suggests that’s actually a myth. Words are extremely powerful and what we say can be quite harmful to ourselves and others around us, causing individuals to be discouraged before they even start something. In addition to putting stereotypes and preconceived notions aside, there are a few specific strategies that I’d like to recommend to help you be successful in this course, and other university courses throughout your degree. Number 1 - Take a growth mindset Having a growth mindset means believing that people can achieve greatness by working at something, versus thinking talented individuals are born that way. To achieve a growth mindset, it requires individuals to shift their thinking, so when they encounter difficulty or a challenge, instead of thinking “well, I don’t get it” and moving on, thinking “I don’t get it yet, but I’ll come back to this later”. Famous research by individuals such as Carol Dweck, have found that having a growth mindset and thinking this way, is extremely powerful and significantly improves learning and performance in many areas of life, including math. Number 2 - Build Connections In cognitive psychology there is a saying “cells that fire together, wire together”. Imagine that every single thing you are trying to learn has a brain cell that is associated with it. When you link concepts, an electrical
One statistics example you are likely already familiar with is the Census run by Statistics Canada* , which is used to help develop, evaluate, and guide decisions about public policies and programs. Learn more about the Census of Population. History of the Census The first Census in what is now Canada took place in 1666. Frequency of the Census Statistics Canada conducts a national Census of population every five years and releases the data two years after its collection. Completion is mandatory by law. Impact The Census is critically important for planning healthcare services. For example, an aging population significantly impacts the needs and cost of the healthcare sector. Definition:* Statistics Canada: A government body required to collect, compile, analyze, abstract and publish statistical information relating to the people of Canada. STATISTICS IN BIOMEDICAL RESEARCH & DRUG TRIALS This content was retrieved from Section 01 Slide 8 of 21 of the online learning module. Biomedical research is another example where statistics provide valuable information. In this case, statistics may be used to identify the probability of a certain outcome occurring (e.g. change in hormone level or phenotype, or a certain dosage of drug resulting in a change in symptoms or adverse event) and what factors may influence that outcome. Listen to Dr. Adams, Director of the BHSc program, discuss the importance of statistics in biomedical research and drug trials. Statistics in Biomedical Research (3:38) Start of Audio Transcript: Hi. My name is Michael Adams. I'm a professor here at Queen's University. I was trained as a pharmacologist, so I'm obviously someone who's very much interested in drugs and how they act on the body. And of course, I've seen a use for statistics for a very long time. And why is that? Because not all the time does it drug work. And why is that? Well, sometimes we don't even know. So statistics in biomedical research is sometimes about whether a drug works at all, whether it works effectively, or how effectively. In my own research, I also do biomedical research. I do fundamental research where I'm looking for underlying mechanisms and again in that I'm after is what I'm talking about making sense. And how do I actually decide whether it makes sense? Well, I'm going to need statistics because that decides whether or not the result is real and whether other people are going to believe my result because unless you have an objective scoring system, you're going to be lacking the ability to have an arm's length review of that
material. So statistics is one of the ways that we get to report on the success of a study, whether it be a clinical study and drug study - for example, antihypertensive lowering blood pressure - or whether it's a basic biomedical sciences study about a mechanism within a cell. So an example of a recent study that has really highlighted the importance of knowing how to use statistics would be a study in which, for example, we're studying the generation of what's called vascular calcification. So vascular calcification is a process where bone forms inside blood vessels. But you know what happens, it happens very suddenly. So if you looked at a time series analysis, all of a sudden this event occurs in some of the situations, not in all. So how do you know, statistically, whether it's an event? Well, you can't just use conventional statistics because it's not a normal distribution pattern. It's all of a sudden, a sudden event and, therefore, a quantal statistic has to be used. So, similarly in drug trials. You can imagine we're all hearing about things like vaccines now and whether they're successful or not successful. And you're hearing that they're so effective. What does that mean, effective? Does that mean that you set the bar and now effective means they never show any signs or symptoms they never get hospitalized? They never have the any signs or symptoms at all and, therefore, the vaccine is perfect? No. They set the bar and they use statistics to decide whether the drug is effective. So why do you need to know about it? Because you're going to be hard pressed to understand the basic literature, unless you understand statistics. Almost every single study that you're going to read, whether it be biomedical, whether it be at the cell level or animal level or the human population level, it's going to tell you about the results based on a statistical analysis because that's going to provide you with their objective score on how good that data is. And you're going to have to know how to read it because not all studies do it well. So you're going to have to have that lens, that perspective, on using your statistical knowledge to determine whether they did the study properly and whether you believe their results. So never think that statistics is for somebody else. You need to have a proficiency and statistics to understand the world around you. In the health sciences, it is very, very key to recognize that your perspective is as valuable as anybody else's. In fact, your take using your own knowledge of statistics may inform somebody else of the next study to do. Hopefully that gives you an insight from my perspective on how statistics is a very important part of my life and is part of why I took my mathematics skill and I applied it in biomedical research. And I tell you it's a lot of fun. End of Audio Transcript. Throughout your undergraduate degree, you will be asked to review, summarize, and critique research articles. This course, along with other research-based courses (e.g. HSCI 270 & BCHM 370), will provide you with the skills and knowledge to be able to critique articles effectively and conduct your own biomedical research in the future. Definition:* Biomedical research: Field of research that explores disease prevention and treatment. BIG DATA, MACHINE LEARNING, & ELECTRONIC MEDICAL RECORDS This content was retrieved from Section 01 Slide 9 of 21 of the online learning module.
Definition:* Evidence-based medicine: The practice of using research-based evidence, clinical expertise, and information about the patient’s values and preferences to make clinical decisions on patient care. Reference: Sackett, DL; Rosenberg, WM; Gray, JA; Hayes, RB; Richardson, WS. (1996). Evidence-based medicine: what it is and what it isn’t. BMJ. Volume 312; pp 71-72. Retrieved September 2020, from: https://doi- org.proxy.queensu.ca/10.1136/bmj.312.7023. THE POWER OF (MIS)INFORMATION This content was retrieved from Section 01 Slide 11 of 21 of the online learning module. In 1998, a study by Wakefield et al. suggested that the Measles, Mumps, and Rubella (MMR) vaccine was correlated with autism spectrum disorder. Despite a small sample size, questionable methodology, and speculative conclusions, the paper received a considerable amount of attention from the media and impacted many caregivers’ decision to vaccinate their children or not. In 2010, after a number of studies disproved the findings, the Lancet journal retracted* Wakefield et al.’s paper; however, the damage was done. MMR vaccination concerns, and concerns about vaccines in general, remain prevalent today. You will learn more about the Wakefield et al. study in Module 02.
Infographic from a 2019 campaign in the United Kingdom which aimed to increase MMR vaccination rates. Definition:* Retracted: In academia, a paper is retracted when it is removed from the academic journal where it was published. References: Rao, T. S., & Andrade, C. (2011). The MMR vaccine and autism: Sensation, refutation, retraction, and fraud. Indian journal of psychiatry, 53 (2), 95-96. Retrieved September 2020, from: https://proxy.queensu.ca/login?url=https://doi.org/10.4103/0019-5545. Daily Mail. (2019). The Numbers That Don’t Lie [Infographic]. Retrieved September 2020, from: https://www.dailymail.co.uk/news/article-7556137/MMR-End-vaccine-ignorance.html. FORENSIC STATISTICS This content was retrieved from Section 01 Slide 12 of 21 of the online learning module. Another place that you see statistics being used is in the criminal justice system. Statistics are used to match biological samples to victims or perpetrators. Statistics can also be used to help investigators and juries identify the likelihood of an event being a coincidence, or if something more malicious took place. The case of Dr. Harold Shipman showcases how statistics can be used to aid criminal investigations. Learn more about Dr. Shipman’s criminal case. Who is Harold Shipman?
What types of evidence do you think would be useful in this case? Your Instructor’s Feedback: Thank you for your response. You will learn how statistical evidence aided the public inquiry into Dr. Shipman’s activities on next slide. DR. SHIPMAN’S PUBLIC INQUIRY DATA This content was retrieved from Section 01 Slide 14 of 21 of the online learning module. The public inquiry provided details on Dr. Shipman’s confirmed victims that had died between 1975 and 1998, including the victim’s age, gender, date, and time of death. The figure is from a book written by Dr. David Spiegelhalter, one of the statisticians involved in the public inquiry. Some conclusions can be drawn by simply looking at the figure. Consider the labels on the left and bottom of the figure. What do you notice? What do you think the dots and bars represent? Once you’ve taken a moment to consider the figure, navigate to the next slide to learn about the conclusions that can be drawn from the data. Reference: Spiegelhalter, D. J. (2020). The art of statistics: learning from data (pg 3). London: Pelican, an imprint of Penguin Books. DEMOGRAPHICS & TIMELINE OF DR. SHIPMAN’S PATIENTS This content was retrieved from Section 01 Slide 15 of 21 of the online learning module. You don’t always need fancy statistical tests to help interpret data. By just looking at this figure, you can draw some preliminary conclusions. Learn about some of the preliminary conclusions that can be drawn from the figure.
Legend As indicated in the legend, the shading of the dots inside the graph indicates whether the victim identified as a man or woman. There are more black than white dots, meaning Dr. Shipman’s victims who died within this time period were mostly women. Use the legend in the figure to view the data for women vs men. Axes Second, the victim’s age is shown on the left side (y-axis) and death on the bottom (x-axis). The bars that are opposite these axes show the same information as the dots, but make it easier to see certain trends. In statistics, there are many different ways to display data. Sometimes showing the data in more than one format can help individuals identify patterns more easily. Right Bars The bars on the right side of the graph show the age of the victims. From this data, you can conclude that across the entire timeline, most of these individuals were in their 70s and 80s. However, by looking at the spread of the dots inside the graph, you can see that earlier in the timeline, these victims were older, and it wasn’t until 1995 that younger victims were dying. Top Bars The bars on top of the graph show the number of deaths across the timeline. You can see a distinct gap around 1990 to 1992, where there were no murders. You know from Dr. Shipman’s background that he was in a joint family practice until 1992, after which he started working on his own. After he was in an independent practice, it appears that the number of deaths increased dramatically.
