Episode 110: [Value Boost] Why You Need Less Data Than You Think

In high-stakes decision-making, waiting for more data is often not an option. Yet many data scientists assume that without a large dataset, meaningful analysis is impossible. The good news is that rigorous, quantitative analysis is possible with far less data than most data scientists realise - in some cases with just a single datapoint.

In this Value Boost episode, Douglas Hubbard joins Dr Genevieve Hayes to share practical techniques from How to Measure Anything that data scientists can start using right now to support high-stakes decisions when observations are scarce and every data point counts.

In this episode, you'll learn:

1. Why a single observation reveals more than you think [01:58]
2. How Laplace's Rule of Succession lets you estimate probabilities from tiny samples [08:25]
3. The Rule of Five and what it reveals about small sample statistics [12:08]
4. The simplest and most overlooked technique for reducing measurement uncertainty [14:07]

Guest Bio

Douglas Hubbard is the founder and president of Hubbard Decision Research and the creator of Applied Information Economics. He has over 35 years’ experience in management consulting focusing on the application of quantitative methods to decision making. He is also the author of How to Measure Anything: Finding the Value of Intangibles in Business and The Failure of Risk Management: Why It’s Broken and How to Fix It.

Links

• How to Measure Anything website
• Connect with Genevieve on LinkedIn
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