As a German statistician, I distinguish between statisticians and "Substanzwissenschaftler" (my best translation so far substantive researchers). While the former know which regression model should be used for the given data and how to estimate such models and what the regression coefficients technical mean, the latter know what variables to study in the model and what the model outcome really tells us about, e.g. the harmfulness of smog.

In this comment it was suggested to use "statisticians" and "scientists", but I disagree as imho statisticians are also scientists.

So: is there an English equivalent to the German word "Substanzwissenschaftler"?

Update: I used the figure from @Canis Lupus, to illustrate what I'm looking for: a word for the group of those scientists, that are not computer scientists, nor mathematicians or statisticians. Figure 1

  • You've used the word pendant twice. The text of your question suggests you're looking for a term. I don't think I've come across the term pendant used that way before. Out of curiosity: can you please add a comment with links to some examples of the word pendant used in the sense of terminology? Thanks.
    – Lawrence
    Apr 20, 2017 at 7:24
  • Wissenschaftler is either a scholar, academic, researcher, or scientist.
    – Mari-Lou A
    Apr 20, 2017 at 7:57
  • 1
    I think the question is trying to get at the difference between handling the statistics and interpreting the results of various tests, perhaps having applied a variety of transformations of the data, versus getting at the substantive situation--reduction in poverty, saving of whales, unemployment consequences of globalization. Climate science, economics, social science. So "data analyst" may be just the opposite.
    – Xanne
    Apr 20, 2017 at 9:13
  • We have the English term "applied science." But theoretical economists, for example, are often interested in what I'd call policy outcomes; although in that field many are also superb statisticians.
    – Xanne
    Apr 20, 2017 at 9:17
  • 1
    @Lawrence "pendant" comes from French meaning something like "counterpart" (dict.cc/deutsch-englisch/Pendant.html). And I thought it is used in English aswell.
    – Qaswed
    Apr 20, 2017 at 9:25

2 Answers 2



After doing a little digging, I had this distinction offered to me:

  • Substanzwissenschaften (or Realwissenschaften) - practical / empirical sciences

  • Formalwissenschaften (or Idealwissenschaften) - theoretical / formal sciences

You might translate "substanzwissenschaftler" to empirical scientist or empirical researcher. Generally, the empirical scientist relies primarily on observations of nature and comes to conclusions that might be used to infer the properties of nature in broader terms. (This is the scientific method.)

A theoretical scientist, on the other hand (including mathematicians), might postulate fundamental properties (which may have been obtained from an empirical scientist) and then predict how nature (and you can include mathematics in this) might behave in broader or more general ways. Theoretical science can also be refered to as pure science.

Then there is the applied scientist, who would try to validate the theoretician's predictions using experiments designed for that purpose.

Contrary to common opinion, sciences like chemistry, biology, and physics, are not simply empirical sciences, and there are many scientists that take on roles in the theoretical and applied science for those disciplines.

Likewise, math is not strictly a pure or theoretical science. Statistics is a form of applied mathematics, where the goal is to use mathematics as a tool to study natural phenomena, rather than to extend the theory of mathematics. Cryptography is a case of applied math that relies heavily on derivations from theoretical math.

Update 2

Gerhard Schurz complicates this somewhat in his paper A Llgemeine Wissenschaftstheorie (A general understanding of science). He wrote the following (with a purposeful reordering of his points by me):

Thus one can classify Substanzwissenschaften as

1) Empirical sciences - all of which refer to empirical data of any kind, that is also for example: historical sciences (historical sources), literary sciences (texts), etc.

2) Experimental sciences - those which do not only have empirical observation data, but rather those in the form of controlled experiments. We come to the significance of the experiment against free "field observation" in the back of chapter 3. This category excludes many "spiritual sciences", such as history sciences, literary sciences, macro-sociology, etc., because one can not experiment here. This includes, for example, experimental psychology, and, of course, the classical natural sciences.

3) "Dissecting" sciences - these are the ones who not only observe their empirical objects or do experiments with them, but also physically decompose them into small parts, or even reverse from them - the "classical" natural sciences such as physics, chemistry, in vitro biology

4) "Speculative" disciplines (non-empirical but synthetic, non-scientific in our sense)

From the first two points, I would imagine Schurz's definition of Substanzwissenschaften is applicable to both empirical and applied scientists. The third point seems to describe what an opportunistic applied scientist might do. Therefore, I think you might prefer applied scientist over empirical scientist, but that might be a matter of personal taste.

The fourth point by Schurz puzzles me a little. It describes what is known as jumping to conclusions, meaning drawing conclusions without the intermediate supporting logic and evidence. (See how Kant compares synthetic to analytic here.) I might put this in the hacking category, myself and I suppose someone might have an argument with Shurz about this one, since it seems to undermine some of the more sacrosanct principles of science, as I understand them.

(I'll leave my earlier answer here for reference, since you thought the figure was helpful for updating your question.)

It might help to pull the word apart and play with it a little. By looking for the words "substantive science", I came up with this chart, along with some of the descriptions for it:

enter image description here

It's what my former data analysis prof called domain knowledge. It's knowledge related to specific facts, to relationships about certain subject matter, not just a technical process. Hacking skills could be the ability to cleverly draw up code from scratch to solve problems; math and statistics would allow you to just do math and stats to data; but substantive expertise would let you use your background in biology to apply those things to finding diseases in DNA codes. If you don't have substantive expertise, you usually don't even know what to do with your technical skills that matters, even if you do have any technical skills. (from Quora - What is substantive expertise in data science?)

Themes common term I've seen for this is subject-matter expert. You might also use domain expert.

There is a good description of this in wikipedia:

A subject-matter expert (SME) or domain expert is a person who is an authority in a particular area or topic. The term domain expert is frequently used in expert systems software development, and there the term always refers to the domain other than the software domain. A domain expert is a person with special knowledge or skills in a particular area of endeavor.

  • How do you call scientists with substantive expertise? Is there something like "substantive expert" or "substantive scientist"?
    – Qaswed
    Apr 20, 2017 at 14:40
  • I'm not sure where the confusion is. For example, geologists, planetologist, meteorologist (the list goes on) are all subject matter experts in the sciences of their domains. They are called scientists. If there is a difference in German, what is a subject matter expert (in German) called? Apr 20, 2017 at 14:52
  • A "subject matter expert" is called "Fachexperte". A person who is expert in his field. This field can be anything and is not limited to scientific field. E.g. a person who is expert in setting up IT-networks is a "Fachexperte", even if he doesn't do scientific research about IT-networks. I call persons in the green cirlce "mathematicians and statisticians"; I'm looking for the name of those in the blue circle. "Scientist" is too broad, as I consider mathematicians and statisticians to be scientists too.
    – Qaswed
    Apr 20, 2017 at 15:07

It sounds like you're looking for a domain expert, also called a subject-matter expert. They are the ones who understand the field of study, as distinct from the (statistical and other) tools to analyse it.

A subject-matter expert (SME) or domain expert is a person who is an authority in a particular area or topic. The term domain expert is frequently used in expert systems software development, and there the term always refers to the domain other than the software domain. A domain expert is a person with special knowledge or skills in a particular area of endeavour. (An accountant is an expert in the domain of accountancy, for example.) The development of accounting software requires knowledge in two different domains: accounting and software. Some of the development workers may be experts in one domain and not the other. - wikipedia

  • This goes into the right direction. But when I hear/read "domain expert" I think more about a technical specialist and not a scientist.
    – Qaswed
    Apr 20, 2017 at 13:44
  • Are you looking for something like a chief scientist? Australia has one.
    – Lawrence
    Apr 20, 2017 at 13:53
  • No, I'm not looking for "chief scientist". But interesting that many Commonwealth realms have one.
    – Qaswed
    Apr 20, 2017 at 14:43
  • @Qaswed In that case, how about returning to statistician vs scientist? If the statistician was (also) a scientist but focused on just the statistical manipulation, it would be fair to label that person a statistician. If you are focusing on what they know, as opposed to what they do, then you'd call that person a scientist, or both scientist and statistician.
    – Lawrence
    Apr 20, 2017 at 15:09

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