Limited time - use promo code: YOURLIGHT22 at checkout

BurnBright Basics to Understanding Data

BurnBright Basics to Understanding Data

3 Seasons

In the BurnBright Basics to Understanding Data we will identify what characterizes bad vs. good data to ensure we make well informed strategic decisions through data analysis. This series includes 3 essential programs:
* "Garbage In: What Makes Bad Data" by Dr. Andrew Hill
* "Representing Reality: What Makes Good Data" by Dr. Andrew Hill
* "How are we doing? Understanding Trends" by Dr. Andrew Hill

Subscribe Share
BurnBright Basics to Understanding Data
  • Introduction to What Makes Good Data

    Episode 1

    You probably have goals for yourself and your teams, and some ideas for achieving those goals. Maybe you also have a strategy for your non-profit or business. That's also super important. Goals and strategy are great, but they only work when you know where you are and can keep track of where you'...

  • Where Does Data Come From?

    Episode 2

    Data doesn't just appear magically. It comes from somewhere. Data may be created from active methods such as experiments, interviews, or surveys, or passive methods such as observation or archival research. Understanding where data comes from is crucial to using it well.

  • Sample vs. Population

    Episode 3

    What's a sample, and why should I care? Knowing whether your data is from a sample or a population helps you understand what questions to ask. Data from samples is easier to get, but it's also potentially biased. So with samples you want to know whether the sampling methods were good--meaning tha...

  • Good Constructs

    Episode 4

    Choosing a good data construct is the most important part of getting good data. The construct is the thing we choose to measure. Hopefully, a construct is a good match for the thing we want to know. But sometimes it isn't. What makes a good construct?

  • Accuracy

    Episode 5

    What's the difference between accuracy and precision? They are not the same, and the difference is important. "Accurate" data gives an impression that closely matches the real thing it is measuring. "Precise" data is measured in a way that gives highly specific measures. For example, if you measu...

  • Representativeness

    Episode 6

    How well does your data match the reality of the the thing you're trying to measure? "Representative" data can be incredibly powerful, because it means your sample is a good match to the reality you are trying to understand. That allows you to analyze the data with a lot more confidence. Ask ques...

  • A Source for Comparison

    Episode 7

    We often prefer to have consistency in our data, but data without variation in crucial measures doesn't tell us anything. For example, if everyone in your office gets the same performance score (4 out of 5), the score will have no value in terms of how it explains actual work performance. In orde...