Business decisions often require organizations to act before they have complete information. Leaders must use what they know about the past to anticipate what might happen next.
Descriptive and diagnostic analytics help explain what happened and why. Predictive analytics builds on that knowledge to estimate future outcomes and helps organizations prepare for them.
Even well-informed predictions are subject to uncertainty, however. Unanticipated changes—from extreme weather to technological advances and market shifts—can affect future outcomes.
That’s why prediction alone isn’t enough. To use predictions effectively, you must also understand how uncertain they are and the assumptions underlying them. Prediction intervals can help.
This guide explains what prediction intervals are, how they differ from confidence intervals, and how you can use them to inform your strategy. It also outlines the steps involved in calculating a prediction interval for linear regression, a common form of statistical analysis.
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What Is Prediction in Business?
In the Harvard Business School Online course Business Analytics—which can be taken individually or as part of the multi-course Core Business Essentials credential program—a business prediction is defined as an estimate of what's likely to happen next. Businesses use predictions to make informed decisions about the actions they should take today to prepare for, or influence, future outcomes.
Imagine, for example, it’s the year 2000 and you run a retail company with brick-and-mortar stores. As e-commerce and large online retailers like Amazon gain popularity, you realize that a growing share of your quarterly sales comes through Amazon’s marketplace, often at lower margins than sales through your physical stores. Your predictive analysis suggests that, if trends continue, more than half of your sales could come through Amazon by 2010, reducing profitability. To retain more control over sales and pricing, you use this prediction to inform the launch of your own e-commerce website.
Although simplified, this example demonstrates how businesses can leverage data to predict future outcomes and inform the strategies they use today. Other common examples of business predictions include:
Predicting future demand to better plan production schedules and required inventory levels
Making financial projections around sales, revenue, and profit to better allocate capital, secure financing, or communicate with investors
Estimating future market penetration to assess whether launching a new product or entering a new market is likely to generate a worthwhile return
What Is a Prediction Interval?
A prediction interval is a range of values that's likely to contain a future outcome at a specified confidence level, based on historical data and a statistical model. Instead of presenting a single predicted value as certain, a prediction interval provides a range of possible outcomes that reflects the uncertainty involved in forecasting.
Prediction intervals are commonly used in regression analysis and time-series forecasting.
Prediction Intervals vs. Confidence Intervals
Although prediction intervals and confidence intervals sound similar, they communicate uncertainty in different ways.
A prediction interval estimates the range in which a single future observation is likely to fall at a specified probability level. A confidence interval, on the other hand, uses sample data to estimate a range of plausible values for a population parameter, such as a mean. In Business Analytics, it's defined as a range of plausible values that's likely to contain the true population value.
Key differences between prediction intervals and confidence intervals include:
They serve different purposes: Prediction intervals estimate where a single future observation is likely to fall, while confidence intervals estimate a population parameter, such as a mean.
Prediction intervals are wider: Confidence intervals estimate the average outcome. Prediction intervals estimate a single future outcome, which may vary from the average. Because they account for this additional variation, prediction intervals are usually wider.
Sample size affects them differently: As sample size increases, confidence intervals generally become narrower. Additional data can also reduce the width of prediction intervals, but they typically remain wider because individual outcomes naturally vary.
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Prediction Interval Formula and Components
In practice, statistical tools and software usually calculate prediction intervals automatically based on the data and model you provide. The exact formula depends on the statistical model. For simple linear regression, the formula is:
Prediction Interval = Predicted Value ± (Critical Value × Prediction Standard Error)
The predicted value is the outcome estimated by your regression model.
The prediction standard error measures the uncertainty associated with predicting a single future observation. A larger prediction standard error indicates greater uncertainty around the predicted value, while a smaller one indicates greater precision.
The critical value is a multiplier that reflects the desired confidence level and the statistical distribution used by the model. For a standard linear regression model with normally distributed errors, you’ll typically use a t-value based on the desired confidence level and the model’s residual degrees of freedom.
Multiplying the critical value by the prediction standard error gives you the margin of error. This is how far the prediction interval extends on either side of the predicted value.
The Value of Making Business Predictions Despite Uncertainty
Business leaders may question the value of predictions if they can never be certain—particularly when a prediction interval is wide.
As explained in Business Analytics, the goal of making business predictions isn’t certainty. It’s to make decisions that perform better than the alternatives, which often rely on intuition, habit, or generalizations. Even an inaccurate prediction can be valuable if it helps your organization prepare, prioritize initiatives, allocate resources, or correct course more effectively.
A prediction interval can be more useful than a single point prediction because it provides a range of plausible outcomes. That allows you to prepare not only for what you believe is most likely to happen, but also for other outcomes within that range.
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An Important Part of Data Literacy
Using business predictions responsibly requires understanding a range of possible outcomes, not just the one considered most likely. Prediction intervals provide that broader context.
Calculating a prediction interval is just one step in data-driven decision-making. Other important steps include collecting an unbiased, representative sample from a target population, conducting the analysis, interpreting the results, and determining how to act on them.
Building these skills can help you evaluate data more confidently, interpret analytical results, and make more informed business decisions. Consider enrolling in an online course like Business Analytics to strengthen your understanding and contribute more strategically in your role.
Are you ready to develop your understanding of business data to better lead your team and organization? Explore Business Analytics—which can be taken individually or as part of our online Core Business Essentials credential program. To learn more about Core Business Essentials, download our free brochure today.