As long as research uses sample data to make inferences (which can hardly be avoided), the problem of interpretation cannot be avoided, regardless of the particular statistical method or even methodology we use. In everything we do, we are forced to make decisions based on assumptions formed in part from our experiences with the behavior of others, which in turn reflect only part of the whole picture. These assumptions reflect conscious or unconscious values (such as giving people the benefit of the doubt, presuming the innocence of an accused person, etc.). This reality does not entail that we cannot strive for scientific detachment once these fundamental pre conditions have been set and the research proceeds. Within the particular “research design,” we may still strive for and insist upon scientific detachment, even though our values have necessarily influenced the design itself.

From the historians' point of view these assumptions must be realistic or they are junk. If we use the assumption of perfect foresight by businessmen to construct data, the question of its empirical validity is crucial. Altering the assumptions, whether about the model or about the data, can make a substantial difference to both the data and the answers.

It is worth pausing for a moment to consider how far our conclusions are affected by considerations which our simplifying assumptions have forced us to neglect.

To proceed from assumptions about an abstract theoretical set-up and from them to draw conclusions about the observable world and to test - by rough or more refined means - whether the conformity with observations is "good" enough, is indeed the time honoured procedure that all empirical sciences, including the natural sciences, have used. I shall therefore not plead guilty of heresy even if I do work with choice-theory concepts that are not invariant under a general monotonic transformation of the utility indicator.

A theory is only as good as its assumptions. If the premises are false, the theory has no real scientific value. The only scientific criterion for judging the validity of a scientific theory is a confrontation with the data of experience.

Human behavior is predictable, but, as in physical science, accurate prediction hinges on the correctness of underlying theoretical assumptions. There is, in fact, no prediction without theory; all managerial decisions and actions rest on assumptions about behavior. If we adopt the posture of the ostrich with respect to our assumptions under the mistaken idea that we are thus “being ‘practical,” or that “management is an art,” our progress with respect to the human side of enterprise will indeed be slow. Only as we examine and test our theoretical assumptions can we hope to make them more adequate, to remove inconsistencies, and thus to improve our ability to predict.

The problem with making assumptions is that we believe they are the truth. We could swear they are real.

[Scientists whose work has no clear, practical implications would want to make their decisions considering such things as:] the relative worth of (1) more observations, (2) greater scope of his conceptual model, (3) simplicity, (4) precision of language, (5) accuracy of the probability assignment.

A pattern of basic assumptions--invented, discovered, or developed by a given group as it learns to cope with its problems of external adaptation and internal integration--that has worked well enough to be considered valid and, therefore, to be taught to new members as the correct way to perceive, think, and feel in relation to those problems.

All models rely on a small set of critical assumptions.

A good (simple) model uncovers them, while a bad (complicated) model obscures them.

our behavior is affected by our assumptions or our perceived truths. We make decisions based on what we think we know

There are here two assumptions about 'evaluations ', which I will call assumption (1) and assumption (2). Assumption (1) is that some individual may, without logical error, base his beliefs about matters of value entirely on premises which no one else would recognise as giving any evidence at all. Assumption (2) is that, given the kind of statement which other people regard as evidence for an evaluative conclusion, he may refuse to draw the conclusion because this does not count as evidence for him.

Assumptions, you see, even when based on sound research, can lead us astray.

When the theory performs well you also think, “Are there parallel results in naturally occurring field data?” You look for coherence across different data sets because theories are not specific to particular data sources. Such extensions are important because theories often make specific assumptions about information and institutions which can be controlled in the laboratory, but which may not accurately represent field data generating situations. Testing theories on the domain of their assumptions is sterile unless it is part of a research program concerned with extending the domain of applications of theory to field environments