Overcoming sample bias
Sample bias is when the choice of data doesn't reflect what is present in the real world. This is also referred to as selection bias. As with many types of bias, this can be completely harmless or very impactful, depending on the application.
In the following diagram, you can see a visual representation of what this looks like. There is hypothetical real-world data on the left that would be helpful (represented as Input z), but for one reason or another, it did not make it into the data that is included in the training dataset:
Figure 6.2 – Sample bias
When we leave this valuable data out, it is detrimental to everyone involved. The previous diagram is more abstract, so let's look at some more concrete examples of what sample bias could look like.
Examples of sample bias
The following items are examples of where sample bias could exist. Of course, this isn't close to an exhaustive list but helps to give...
