Adding anomalies to the Dataset Recently I have been trying different Scikit-Learn anomaly detection clustering methods, like 


DBSCAN 
Isolation Forest.


Based on how many training data I use, how I tweak on the algorithms 

Example in DBSCAN I play around this min_samples and eps distance.

My problem
Now I am getting different results, however this is where the problem is coming. I don't know how many anomalies I am supposed to get, so this leads to me not being sure what is working best.

I have read a it into adding anomalies yourself into the dataset. Is this a thing?
Meaning that I would know if the found anomalies from the algorithm are the same as the one that have been added to the dataset.

I might have gotten this wrong.

The other problem is that the column I want to check for anomalis has numbers that really differ in magnitude so I also wouldn't what numbers I should add as anomalies.

Would appreciate some help.
Without labels for which data points are anomalies, you have no way to know how many anomalies you should get.

The closest thing to adding anomalies to the dataset is using synthetic data for anomaly detection. I have seen some practitioners doing this, and failing. When you create the synthetic data, it matches your inductive bias*. The real world very rarely matches that inductive bias*. Afterwards, you can easily find a clustering algorithm with the same inductive bias* which can flag your synthetic data as anomalies. However, when that algorithm is deployed it will fail miserably.

*in plain language, inductive bias is the assumptions you have of how the world should be.