What's the difference between statistics and informatics? We always say that statistics is just dealing with data. But we also know that informatics is also getting knowledge from data analysis. For example, bioinformatics people can totally go without biostatistics. I want to know what is the essential difference between statistics and informatics.
 A: My view is that while there is a fair amount of overlap between the fields there are also key differences.  In general a statistics student (in the higher degrees) will take more theory classes (math and mathstat) than the informatics student, but the informatics student will learn more of the computing (especially the database part) side.
Developing a new statistical test would fall more to the statistician than the informaticist, but designing an interface for a user to enter data and produce tables and plots would fall more to the informaticist than the statistician.  
To the statistician the computer is a tool to help with statistics.  To the informaticist statistics are a tool to help collect and distribute information (via computer generally).
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To exand, here is an example.  I have worked on projects with informaticists (I am the statistician) where a medical doctor wants to have a system where information on patients is used to predict their risk of some condition (developing a blood clot for example) and wants to receive some form of alert to let them know about the risk.  My role in the project (the statistician role) is to develop a model that will predict risk given the predictor variables (a logistic regression model is one such model).  The informaticist role in the project is to develop the tools that collect the predictor variables, use my model on them, then send the results to the doctor.  The data may be collected from an electronic medical record, or through a data entry screen for a nurse to fill in or others.  The alert to the doctor may be a pop-up on the computer or a text message sent to their cell phone or others.
Now I (and many other statisticians) know enough of the programming that I could query a database to get the predictors and create some type of alert, but I am happy to leave that to the informaticists (and they are better at it anyways).  There are informaticists that know enough statistics to fit the logistic regression model.  So a simple version of this project could be done by only a statistician, or only an informaticist, but it is best when both work together.  If you look at this project and think the modeling part is the fun part and the data collection, alert and other interfaces are just tools to move the information to and from the model then you are more of a statistician.  If you see designing the interface, optimizing the data retrival, testing different types of alerts, etc. as the fun part and the statistical model as just a tool to convert one part of your data into the other part, then you are more of an informaticist.
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A: Statistics infers from data; Informatics operates on data. Of course they overlap, but the question of which has the larger scope has no answer.
A: Excellent question!!
I heard several times that bioinformaticians can go without biostatistics, or even without statistics. That's perfectly true until it becomes false. In my opinion, general lack of statistical knowledge has disastrous effect in the field, as shown by Keith Baggerly. I could also observe that lack of basic knowledge in statistics (and linear algebra) is the cause of stagnation of bioinformaticians in the long run: without a deep knowledge of the theory, they tend to reinvent the wheel and resort to ad hoc solutions that solve nothing but their own problem.
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But now, to answer your question, I agree that overall, statistics can't do without computers those days. Yet, one of the major aspects of statistics is inference, which has nothing to do with computers. Statistical inference is actually what makes statistics a science, because it tells you whether or not your conclusions hold up in other contexts.
In short, you can analyze the hell out of your data, you will still need statistics to know the validity of the predictions or decisions you will make based on your analyses.
