What does it mean that a dataset is "biased"? What does it mean when people within the field of Machine Learning talk about biased datasets? I thought it was only estimators that could be biased.
In documenting work I have done, I am being asked:
"All datasets are biased in some way. How is your dataset biased?"
I have no clue what they mean. Also my dataset is population data so I have a hard time interpreting it as sample selection problem ...
The objective of the analysis is primarily descriptive. I will construct three indeces and combining them. The three indeces are 1) Housing price index, 2)  Transportation time index and 3) Wage index.
I know, that even with population data there is a sample selection problem for the housing prices - because not all houses are traded every year and the subsample that are traded are most likely not a random sample of the total stock of housing.
I am not generalizing beyond the time period for which I have data which is a 30-year period.
 A: From working as a statistician where my main role is a consultant to the subject matter experts that also work for us, I have noticed that people with less of an understanding of statistics throw the word bias out when they just want to say something is wrong.
They really have no idea what they are saying when they are saying something has bias and will say it anytime they are concerned as a kind of catch all even if the context has nothing to do with bias. Many times when I am explaining something to someone they respond "what about bias" even though it has nothing to do with conversation at hand.
I suspect this may be the case with your scenario specifically when you see them saying something like:
"All datasets are biased in some way. How is your dataset biased?"
Which is certainly not true.
Just a note that this gets multiplied when we start talking about buzz words like machine learning. I've had people give me a dataset and ask me "can you machine learn this....".
A: The term “biased” simply means, that your sample is not chosen randomly.
This is similar to a biased dice, which produces number 6 more often than the other numbers.
It is always difficult how to obtain an unbiased sample, but some notoriously known errors are:

*

*non-response bias (some people respond, some not),

*voluntary response bias (questions attract very opinionated people),

*volunteer bias (volunteers doesn't represent the whole population),

*survivorship bias (concentration on the “survivors” of a particular process)

*availability bias (selecting easily available people / things)

Here and here are listed and explained some other types of biases.
A: Perhaps you know that when iPhone users text each other, there is a blue “send” arrow instead of the green that you get when you text someone who uses another type of phone. To collect data, you randomly text numbers, but only if the arrow is blue. Your sample is biased, since you’ve excluded people who, for whatever reason, do not use iPhones. Perhaps political viewpoints influence phone purchase decisions. If you were texting about something political, you’ve excluded certain viewpoints.
