Moments that Matter, Part 1 – Simple is Good (The Rule of Five)

When we think about data, many of us imagine huge spreadsheets filled with endless rows of numbers. It’s easy to assume that the bigger the set of data, the more accurate our insights will be. But very often, a simpler method not only gives us a great result, but it’s much easier and less invasive to implement. And in this case, “simpler” means that you only need five data points to get a very good idea of what’s going on. That’s where the Rule of 5 comes in.

What is the Rule of 5?

The Rule of 5 is a simple statistical principle that suggests if you randomly select just five data points from a well-distributed dataset, there’s a high likelihood that the median of those five points will be close to the median of the entire dataset. In other words, even with a small sample size, you can get a surprisingly accurate picture of the bigger picture.

Of course, this doesn’t mean that every dataset will behave perfectly with just five samples. But in many cases, especially when data is not heavily skewed, the Rule of 5 allows us to make reasonable estimates without accessing and sifting through mountains of information.

How Does the Rule of 5 Work?

Imagine you’re trying to estimate the median income of employees in a company of 10,000 people. Instead of collecting data from every single employee (very invasive, and could take forever), you randomly select five employees and record their incomes. Chances are, the median of these five incomes will be fairly close to the actual median of all 10,000 employees.

Why does this work? Because medians are less sensitive to outliers than averages. As long as the dataset isn’t wildly unbalanced, a small, well-chosen sample can provide valuable insights.

Real-World Applications of the Rule of 5

The Rule of 5 is used in various fields to make quick, practical decisions without drowning in data:

  • Business Decision-Making: Instead of analyzing thousands of customer reviews, randomly selecting five and assessing their general sentiment can often reveal the overall trend.
  • Quality Control: Manufacturers use small sample sizes to check product defects, knowing that a few good or bad units often represent broader trends.
  • AI and Machine Learning: When training AI models, small sample testing can quickly indicate whether an algorithm is on the right track before scaling up to larger datasets.

    How the Rule of 5 Helps AI and Businesses

    One of the biggest challenges in AI is managing big data—the massive amounts of information that companies collect daily. Emails, chat logs, customer service interactions, and transaction records pile up at an overwhelming pace. Processing all this data takes time, storage space, and computing power.

    The Rule of 5 helps businesses and AI developers by showing that there’s often little statistical benefit in analyzing an entire dataset when smaller, well-chosen samples provide nearly the same insights. Instead of combing through every email ever sent in a company, AI can analyze a handful of recent, relevant emails and still make strong predictions.

    By reducing the amount of data AI needs to process, businesses save on storage costs, computing resources, and time—allowing them to focus on action rather than endless analysis.

    Less Can Be More

    The Rule of 5 is a powerful reminder that bigger isn’t always better when it comes to data. Whether making business decisions, streamlining AI processes, or simply trying to get a quick read on a situation, a small but well-selected sample can provide incredible value.

    Here’s a mathematical example of the Rule of 5, take a look at the numbers to see how simpler can be better:

     

    The Rule of Five in statistics is a heuristic stating that a random sample of five items from a population has a 93.75% chance of containing the population’s median value. This suggests quick and simple data sampling can be useful.

    The probability of a randomly selected value being above or below the median is 50% in any population. For all five values to be either above or below the median (and therefore the median not being between the min and max), the probability is (0.5)⁵ + (0.5)⁵= 0.0625, which means that the probability of the median being within the sample is 1 – 0.0625 = 0.9375 or 93.75%.