When submitting a paper to a highly profiled publication, like Nature or Science, author contributions must be clarified. This may include study design; data acquisition, analysis, and interpretation; draft preparation and/or critical editing and review of the manuscript. Among these aspects, I was wondering how data analysis and data interpretation are different? Consider the boundaries may not be quit clear, because more often it is difficult to analyze data to find true patterns without being able to interpret those patterns.
I agree that the boundaries are not quite clear in that it is impossible to analyse a set of data without giving some sort of interpretation to them at the same time.
The requirement however doesn't mean that you have to identify a single author for each contribution. For example, you can say that author A contributed to data acquisition, author B to both data analysis and interpretation.
In an experiment, data acquisition can simply mean running the tests without processing the data into more meaningful plots or graphs. The latter falls into the category of data analysis. Discussing what the data mean comes into interpretation. While interpretation cannot be absent in any analysis, and so I cannot imagine an author contributing to analysis but not to interpretation, the reverse may be possible. An author can contribute to interpretation without contributing to analysis.
I'm frequently on the "data analysis" end of this spectrum when I work together with biological psychologists (or psychological biologists). Basically, I run the analyses, do all kinds of diagnostic checks, plot enlightening plots and discuss all this with the biologists and the psychologists. They in turn look at my plots and know what results make biological/psychological sense, e.g., which biomarkers are frequently associated with what disease or other, or what the recent literature has to say about some specific relationship. Or, much more basic, the biologist can tell me which measurement makes sense, and which one has to be a measurement error.
This is the relationship between data analysis and data interpretation.
I think that this kind of division of labor makes a lot of sense. Statisticians simply know a lot more about statistics than do psychologists, doctors or biologists. And (sorry) I have seen very disheartening things happen if subject matter experts think they know enough statistics to run complicated analyses themselves. Yes, some psychologists do have an excellent grasp of statistics - but most simply don't. And that's how it should be. After all, I don't expect my car mechanic to be able to repair my refrigerator, either. (It should go without saying that I don't think statisticians shouldn't arrogate any specific subject matter expertise to themselves, either.)
The relationship between subject matter expertise and statistical knowledge is a frequently discussed topic in what is nowadays called Data Science. I have expounded on my point of view here.
You should certainly be able to do both. Data analysis incorporates data management and statistical analysis. In data interpretation you find out what the data means, how they throw light on your research questions. Data analysis is necessary for interpretation; however, data analysis is also guided by your anticipations, i.e. your research questions which again, hopefully, will relate to what the data will tell you (because you designed your data sampling so).