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Course: Statistical Thinking and Cognitive Bias: The Fundamentals

Statistical Thinking and Cognitive Bias: The Fundamentals

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  • Certificate on Completion
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  • Self-Paced
About this Course

Do you want to see the world more clearly? Learn to think like a statistician and question the numbers all around you? Make better decisions at work, when investing, and in life? Then you are in the right place.

This course is not a regular introduction to statistics. There will be very little math so you won’t be asked to complete lots of mathematical problem sets. The focus of the course will be on the intuition and practical application of statistics in making better decisions and judgments. We will explore the fundamental ideas and concepts of statistics but with with everyday examples, answering questions such as: if correlation does not equal causation, then what does? have humans really wiped out 60 percent of animals? and do 9 out of 10 dentists actually recommend this toothpaste?

You will learn how not to be fooled by data visualizations, and how an understanding of probability can change the way you view everything from prosecuting criminals to financial crises.

The course has two sections diving into the world of cognitive bias and the work of Hans Rosling on Factfulness thinking. Our inherent mental biases can affect the way we perceive and interact with the statistics we encounter every day; whether in the news, on social media, or in advertisements. The goal of this section is to learn how to spot these logical fallacies so we can keep them at bay and interpret the world around us more objectively.

In the penultimate section, we shall encounter the tricky world of inference, causation, and the trusty work-horse of statistics; regression analysis. This section will explore everything from buying apples, to p-hacking, and to what caused physician John Ioannidis to proclaim that "most published research findings are false".

The final section will include a bonus taster session of my next course looking at prediction and forecasting; exploring why predictions fail, how they can succeed, and if perfect prediction will ever be possible (or indeed desirable).

With a world of information now at our fingertips, being able to think statistically is an essential skill for all those living in the 21st century. Indeed as Herbert George Wells said, “statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write” – and that day has come.

Basic knowledge
  • No prerequisites other than a willingness to learn, challenge, and critique!
What you will learn
  • Basic statistics applied to our world, work, and everyday life
  • Basic probability applied to our world, work, and everyday life - including Bayes' theorem
  • New insights from behavioral economics, specifically how our mental biases can lead to misinterpreting statistics
  • The ability to make more informed decisions
  • The ability to think critically about statistics
  • A clearer, global worldview
Curriculum
Number of Lectures: 62
Total Duration: 03:19:17
Introduction
  • What is Statistics and Statistical Thinking?  

    In this lecture I will explain what is statistical thinking and give an overview of the course.

  • How to Construct a Statistic?  

    In this lecture I give a general overview of the typical process of constructing a statistic.

  • Session 1: Wrap-up  
The Uses and Misuses of Statistics 1: Sampling
  • The Basics of Sampling  

    In this lecture I go over the basic types of sampling, highlighting how even the gathering of data involves human judgement.

  • Selection Bias  

    What is selection bias? In this lecture I explain what it means and give you some common examples.

  • Sample Size  

    In this lecture I talk about the importance of sample size when trying to draw conclusions from data.

  • Dubious Definitions and Questionable Categories  

    Here we look at how the way in which statistics are defined and categorized can alter the conclusions.

  • If at First You Don't Succeed... Multiple Sampling  

    Didn't get the answer you wanted? Try again! In this lecture we discuss multiple sampling.

  • Exercise  
  • Session 2: Warp-up  
The Uses and Misuses of Statistics 2: Descriptive Statistics
  • What Are Descriptive Statistics?  

    An introduction to descriptive statistics.

  • Beware of the Average  

    How can averages be misleading? In this lecture we find out.

  • Beware of the Point Estimate: Don’t Ignore Dispersion  

    Point estimates may not always accurately represent the data - in this lecture we discuss some of the other thing to consider.

  • Problematic Percentages  

    Percentages are notoriously tricky - in this lecture we discuss the main things to look out for when being presented with percentages.

  • Ignoring Scale: The Base Rate Fallacy  

    In this lecture we discuss the base rate fallacy, a common pitfall in statistical thinking.

  • Ignoring Context: Stats do Not Exist in a Vacuum  

    No statistic can be fully understood without context - this lecture looks at the importance of not ignoring it.

  • Units of Measurement and False Comparisons  

    When interpreting any statistic it is important to be clear about what units are being used. Without that, you may fall victim to false comparisons.

  • Session 3: Wrap-up  
Cheating Charts: How Not to be Fooled by Data Visualizations
  • Common Types of Charts  

    In this lecture we will go over the common types of data visualizations.

  • Audacious Axis  

    In this lecture we look at how interfering with the axis in charts can distort your perception of the data.

  • Misleading Graphics  

    Data graphics are becoming increasingly popular. In this lecture we look a few ways in which they may misleading.

  • Session 4: Wrap-up  
What are the Chances? The Uses and Misuses of Probability
  • Probability Basics  

    In this lecture we re-visit the basics of probability.

  • Expected Value/Loss  

    Expected value is the combination of value and probability. In this lecture we look at how to use expected value to think about playing the lottery, insurance, and investments.

  • Black Swans and Fat Tails  

    In this lecture we talk about unlikely and seemingly unpredictable events, or 'black swans'.

  • Wiggle Room: The Case of the Baltimore Stockbroker  

    In this lecture we look at wiggle room, and the fable of the seemingly clairvoyant stockbroker.

  • Regression to the Mean  

    In this lecture we look at regression to the mean;the tendency for things to return to the long term average.

  • Clustering  

    In this lecture we talk about our natural inclination to see patterns where there are none.

  • Faulty Thinking About Joint Probabilities  

    Probability can be tricky enough on its own, so when you need to combine the probabilities of multiple events there is plenty of room for fallacious thinking.

  • Introducing Reverend Bayes  

    It might not be enough to simply have an estimate of probability. Sometimes you may benefit from incorporating your prior beliefs and updating that prior as you encounter new evidence - this is where Bayes comes in.

  • Session 5: Wrap-up  
The Uses and Misuses of Statistics: 3 Inference and Regression
  • What are Inferential Statistics?  

    In this lecture we cover the basics of inferential statistics.

  • Linear Thinking: A Precautionary Note  

    This lecture is a precautionary note warning against falling into the trap of linear thinking. In it we look at a study that made a prediction of US obesity rates.

  • Correlation Doesn't Equal Causation  

    In this lecture we look at the most famous saying in statistics: "correlation doesn't equal causation".

  • Statistical Significance: A Basic Overview  

    In this lecture I will run through the basics of one the most important concepts in statistical inference: significance.

  • Problematic P–Values  

    Following on from the previous lecture, in this video we look at p-values - an important tool in determining the significance of results.

  • Publication Bias and the Replication Crisis  

    After this lecture you will understand what it is meant by publication bias and be aware of the replication crisis.

  • Regression Analysis: A Brief Introduction  

    Regression analysis is the bread and butter of statistical analysis. In this lecture I will give you a basic overview of what it involves.

  • Regression Pitfalls and Biases  

    Regression is a great tool, but it isn't perfect! Here we will run through some of its shortcomings.

  • Cause and Effect  

    In wrapping up this section, I will give you an introduction to thinking about cause and effect in a structured and robust way.

  • Session 6: Wrap-up  
Cognitive Bias and Statistics 1
  • What is Cognitive Bias?  

    Our mental biases can get in the way of interpreting statistics - in this section we will look at some of the most common ways that happens.

  • Confirmation Bias  

    Confirmation bias is the mother of all biases - in this lecture you will learn what it is and what you can do about it.

  • Conjunction Fallacy  

    Here we will look at the conjunction fallacy where specific conditions are seen as more probable than general ones.

  • Authority Bias  

    We are more prone to believe something when it comes from an authority - this is the authority bias.

  • Exponentials  

    We do not have a natural grasp of exponentials - in this lecture we look at some examples.

  • Framing Bias  

    The way something is phrased/ framed can change the way you perceive it - in this lecture I go through some famous examples.

  • Session 7: Wrap-up  
Cognitive Bias and Statistics 2: Hans Rosling, Factfulness and Our Ten Instincts
  • Hans Rosling, Factfulness and Our Ten Instincts  

    In this section we look at the work of Hans Rosling, particularly that of Gapminder.org and his recent book Factfulness.

  • Gap Instinct  

    We are prone to dividing the world into two binary categories - this is the gap instinct.

  • Negativity Instinct  

    We are all prone to see the negative rather than the positive - an instinct that can stop us from seeing the world as it actually is.

  • Straight Line Instinct  

    We have already talked about the dangers of linear thinking - in this lecture we will apply that to a global issue.

  • Fear Instinct  

    Fear stops us from thinking straight, in this lecture how to keep things in perspective.

  • Size Instinct  

    In this lecture you'll learn how to keep things in perspective.

  • Generalization Instinct  

    Generalization is a helpful tool we use to simplify the world, but it can lead us astray if used too liberally.

  • Destiny Instinct  

    Not all things are bound by destiny, don't be fooled into thinking they are.

  • Single Perspective Instinct  

    When all you have is a hammer, everything looks like a nail.

  • Blame Instinct  

    When things go wrong (or right) we are quick to point the finger, unfortunately this stops us seeing the true causes of things.

  • Urgency Instinct  

    Our inclination to want to act or decide with urgency can interfere with effective decision making - in this lecture we look at how to keep this instinct at bay.

  • Session 8: Wrap-up  
BONUS/TASTER: The Art and Science of Prediction
  • Prediction and Forecasting: An Overview  

    This lecture introduces my next course on prediction and forecasting.

Conclusion, Next Steps, and Congratulations!
  • Conclusion, Next Steps, and Congratulations!  

    Thank you and goodbye!

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