Skewed distribution bell curve highlighting the mode, with a cityscape background.

Are You Forecasting Your Future Wrong? Why Central Tendency Isn't Always the Answer

Lena Voss in Business & Economy December 2025 • 4 min read.

"Unlock the secrets to better predictions: Discover how understanding measures beyond the average can reshape your approach to personal and economic forecasts."

We all try to predict the future, whether it's estimating our income, anticipating market trends, or simply planning our week. Often, we rely on a single 'best guess,' usually some form of average. But what if that average isn't telling the whole story? What if our reliance on traditional measures of central tendency is leading us astray?

Economic surveys, for instance, often ask respondents to provide a single point forecast for complex variables like future earnings. The problem? Individuals might instinctively report different statistical quantities, like the mean (average), median (midpoint), or mode (most likely outcome). This ambiguity becomes critical when these measures diverge, especially in skewed or asymmetrical distributions. Think about it: is your 'best guess' really the average, or is it something else?

Groundbreaking research is challenging our assumptions about how people make forecasts. Instead of blindly assuming everyone aims for the average, new evidence suggests that many people, perhaps unknowingly, are using the 'mode' – the most frequent or likely value. This shift in perspective has profound implications for how we interpret forecasts and make decisions based on them.

Beyond the Mean: Why Mode Forecasts Matter

Skewed distribution bell curve highlighting the mode, with a cityscape background.

The conventional wisdom in economics and statistics assumes that people provide mean forecasts. However, studies increasingly show that point forecasts often align better with the mode. Central banks, for example, frequently publish inflation forecasts that correspond to the mode, representing the most probable outcome rather than the mathematical average. Similarly, individual survey respondents' income expectations may reflect the earnings they are most likely to achieve, not necessarily the average of all possible outcomes.

Why does this distinction matter? Because relying on the wrong measure of central tendency can lead to misinterpretations and flawed decisions. Understanding whether forecasts represent the mean, median, or mode allows for more accurate evaluation and better-informed policy decisions. It also helps individuals make more realistic financial and career plans.

Symmetric vs. Skewed Distributions: In symmetrical distributions, the mean, median, and mode coincide. However, real-world variables like income growth and inflation rates often exhibit skewness, where these measures diverge.
Elicitability: The mean and median are 'elicitable,' meaning they can be derived from minimizing a specific loss function. The mode, however, lacks a strict identification function, making it more challenging to test its rationality.
Asymptotic Elicitability: Recent research introduces the concept of 'asymptotic elicitability' for the mode, using a sequence of elicitable functionals that converge to the mode under certain conditions.

The challenge lies in identifying which measure of central tendency people are actually using when they provide a forecast. Innovative testing frameworks are emerging to address this identification problem, allowing researchers to analyze forecasts without knowing whether they represent the mean, median, or mode.

The Future of Forecasting: Embracing Uncertainty and Nuance

The next time you encounter a forecast, remember that the 'average' might not be the whole story. By acknowledging the potential for mode-based thinking and using advanced analytical tools, we can unlock a deeper understanding of economic trends and individual expectations. Embracing this nuanced approach to forecasting empowers us to make more informed decisions, navigate uncertainty, and prepare for a future that rarely unfolds exactly as 'average' suggests.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.1910.12545,

Title: Testing Forecast Rationality For Measures Of Central Tendency

Subject: econ.em econ.gn math.st q-fin.ec stat.th

Authors: Timo Dimitriadis, Andrew J. Patton, Patrick W. Schmidt

Published: 28-10-2019

Everything You Need To Know

What are the key measures of central tendency, and how do they differ?

The main measures of central tendency are the mean, median, and mode. The mean is the average, calculated by summing all values and dividing by the number of values. The median is the midpoint, the value that separates the higher half from the lower half of a data set. The mode is the most frequently occurring value in a dataset. These measures coincide in symmetric distributions, but they diverge in skewed distributions, which can significantly affect forecast interpretation. For example, in an income distribution, the mean might be higher than the median due to a few high earners, while the mode could represent the most common income level.

Why is relying on the mean in forecasting potentially misleading?

Relying solely on the mean in forecasting can be misleading because it doesn't always represent the most likely outcome. Research suggests people may be using the mode, or the most frequent value, when making forecasts. When distributions are skewed, the mean, median, and mode can differ significantly. Therefore, assuming that people are reporting their 'best guess' as the mean can lead to misinterpretations of economic trends and individual expectations. The mode provides a more accurate depiction of the most likely result when compared to the mean, especially in non-symmetric data.

How do mode forecasts work, and how are they used?

Mode forecasts focus on the most probable outcome rather than the average. Central banks, for instance, often publish inflation forecasts that align with the mode. Individual survey respondents might base their income expectations on the mode, reflecting the earnings they are most likely to achieve. This approach acknowledges that individuals may naturally think in terms of the most likely or frequent value, providing a more realistic and insightful perspective than the mean, particularly in skewed distributions. This perspective is useful for economic analysis and policy-making, allowing for more informed decisions and planning.

What is the difference between symmetric and skewed distributions, and why does it matter for forecasting?

In symmetric distributions, the mean, median, and mode all coincide, providing a clear picture of the central tendency. However, real-world variables, such as income growth and inflation rates, often exhibit skewness. In a skewed distribution, the mean, median, and mode diverge. For instance, in a right-skewed income distribution, the mean is higher than the median and mode because of a few high earners. This divergence is crucial for forecasting because it impacts how we interpret forecasts and make decisions. If we assume forecasts are based on the mean when they are actually based on the mode, we may misinterpret market trends and the distribution of outcomes.

What is asymptotic elicitability, and why is it important for understanding mode forecasts?

Asymptotic elicitability is a concept that introduces a method for identifying the mode, which is usually challenging because it lacks a strict identification function. The research introduces the concept of 'asymptotic elicitability' for the mode, which involves using a sequence of elicitable functionals that converge to the mode under specific conditions. This innovation helps researchers and economists to analyze forecasts more effectively without knowing whether those forecasts are based on the mean, median, or mode, improving the overall understanding of economic trends and individual expectations and increasing the accuracy of forecasts.