Understanding Mean Absolute Error (MAE) and Mean Squared Error (MSE): A Beginner-Friendly Guide with Business Examples

Last Updated on June 6, 2026 by Statnzee Team

Machine learning models make predictions, but how do we know whether those predictions are good or bad?

This is where evaluation metrics come in. Two of the most commonly used metrics for regression problems are:

• Mean Absolute Error (MAE)
• Mean Squared Error (MSE)

In this article, we’ll explore what these metrics mean, how they are calculated, and why businesses use them to evaluate predictive models.

What Is an Error?

An error is simply the difference between an actual value and a predicted value.

For example:

Actual Sales	Predicted Sales
100	90

Error = Actual − Predicted

Error = 100 − 90 = 10

If a model predicts perfectly, the error is zero.

The larger the error, the worse the prediction.

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Understanding Mean Absolute Error (MAE)

MAE measures the average size of prediction errors.

It answers a simple question:

On average, how far away are the predictions from the actual values?

The formula is:

Notice the absolute value signs. They ensure that negative and positive errors do not cancel each other out.

Example: House Price Prediction

Suppose a model predicts house prices:

House	Actual Price	Predicted Price
A	₹10 lakh	₹11 lakh
B	₹15 lakh	₹13 lakh
C	₹20 lakh	₹22 lakh

Errors:

House	Error
A	-1
B	2
C	-2

Absolute errors:

House	Absolute Error
A	1
B	2
C	2

MAE:

This means the model’s predictions are off by approximately ₹1.67 lakh on average.

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Why Use Absolute Values?

Imagine we did not use absolute values.

Errors:

• -1
• 2
• -2

Adding them:

Average:

This incorrectly suggests that the model is making very small mistakes.

Absolute values prevent positive and negative errors from cancelling each other.

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Understanding Mean Squared Error (MSE)

MSE is another popular metric used to evaluate prediction quality.

Instead of taking the absolute value of errors, MSE squares them.

Formula:

Squaring has an important effect:

• Small errors remain relatively small.
• Large errors become much larger.

As a result, MSE heavily penalizes large mistakes.

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Example: Calculating MSE

Suppose our prediction errors are:

• 1
• 2
• 20

Step 1: Square Each Error

Error	Squared Error
1	1
2	4
20	400

Step 2: Add the Squared Errors

Step 3: Divide by the Number of Errors

Therefore:

MSE = 135

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Comparing MAE and MSE

Using the same errors:

• 1
• 2
• 20

MAE

MSE

Notice how the large error (20) becomes 400 after squaring.

This causes MSE to increase dramatically.

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Why Do Data Scientists Use MSE?

Suppose a logistics company predicts delivery times.

Actual	Predicted	Error
30 min	31 min	1
45 min	47 min	2
60 min	80 min	20

The 20-minute mistake is much more serious than the 1-minute or 2-minute mistakes.

MSE emphasizes this large error, helping organizations identify models that occasionally make very poor predictions.

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Business Use Cases of MAE

Retail Inventory Forecasting

Retailers forecast product demand.

If MAE = 5 units, forecasts are wrong by about five products on average.

This helps businesses decide:

• How much inventory to stock
• When to reorder products
• How much warehouse space is needed

Real Estate Valuation

Property websites estimate house values.

If MAE = ₹50,000, estimates are typically within ₹50,000 of actual selling prices.

Sales Forecasting

Companies predict future sales to manage budgets and resources.

A lower MAE means more reliable planning.

Energy Consumption Forecasting

Power companies estimate future electricity demand.

Accurate forecasts reduce shortages and unnecessary generation costs.

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Business Use Cases of MSE

Delivery Time Prediction

Late deliveries can significantly affect customer satisfaction.

MSE penalizes large timing mistakes more heavily.

Financial Forecasting

Banks forecasting cash requirements may prefer MSE because large prediction errors can be costly.

Manufacturing

Factories predicting machine failures often want to avoid rare but severe prediction mistakes.

MSE helps highlight these large errors.

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When Should You Use MAE?

MAE is useful when:

• You want an easily understandable metric.
• All errors should be treated equally.
• Business users need a simple explanation.

Example:

“On average, our sales forecasts are off by 10 units.”

This is easy for managers to understand.

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When Should You Use MSE?

MSE is useful when:

• Large mistakes are especially costly.
• You want to penalize outliers heavily.
• You are training many machine learning algorithms, since MSE is mathematically convenient for optimization.

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Final Thoughts

Both MAE and MSE measure prediction accuracy, but they focus on different aspects of model performance.

• MAE measures the average size of mistakes.
• MSE measures the average squared mistake and heavily penalizes large errors.

A good way to think about them is:

• MAE asks: “How wrong are we on average?”
• MSE asks: “How much should we worry about big mistakes?”

Understanding both metrics helps data scientists and business leaders choose models that best fit their real-world objectives.