Incorrect Data
Incorrect data means that a value exists in the dataset, but the value itself is wrong, invalid, or does not represent the real-world situation correctly.
Incorrect data is information that is present but wrong.
This is different from a missing value. With missing data, we have no value. With incorrect data, we have a value, but that value is not correct.
Missing vs Incorrect Data
These two problems are easy to confuse.
We don't know the person's age.
A value exists, but it is probably wrong.
The key difference is simple:
Simple Example: Age
Suppose a customer dataset contains these ages:
An age of 250 is probably not a realistic customer age.
The problem is that the dataset contains a number, so the value is not technically "missing." The value itself is incorrect.
How Incorrect Data Can Enter a Dataset
Incorrect values can come from normal human or system errors.
Another Example: House Price
Imagine we are building a model to predict house prices.
Suppose House C is an ordinary residential property and the intended price was actually $450,000.
The dataset now contains an incorrect value that is dramatically different from reality.
If we train a model without investigating the value, that incorrect information becomes part of the training data.
Incorrect Data Can Change What the Model Learns
A machine learning model learns patterns from the examples it receives.
If some examples contain incorrect information, the model can learn relationships that do not represent reality.
This is why we should inspect suspicious values before using the dataset for training.
How Do We Handle Incorrect Data?
The correct action depends on why the value is incorrect and whether the real value can be determined.
Real-World Example
Imagine an e-commerce company has customer ages:
We should not immediately tell the model that Priya is 290 years old.
Instead, we investigate the source. Perhaps the actual value was 29 and someone entered an extra zero.
Now the dataset contains the verified value instead of the incorrect value.
Don't Confuse Incorrect Data With Unusual Data
A value that looks unusual is not automatically incorrect.
For example, suppose a dataset contains a person's annual income of ₹5 crore. That value may look unusual compared with most customers, but it could be completely real.
This distinction becomes especially important when we learn about outliers in the next topic.
Don't Let Wrong Information Become Training Data.
Incorrect data contains a value, but that value does not correctly represent reality. Find suspicious values, investigate them, and correct or remove them when there is enough evidence that they are wrong.
Which One Is Incorrect Data?
Age = 250 is likely incorrect for a normal customer dataset, but it should still be investigated. Age = 95 is unusual but could be completely valid. Never assume that an unusual value is automatically wrong.