I always cringe when I see a statement in a text or website such as “the research hypothesis, symbolized as *H*_{1 }, states a relationship between variables.” No! No! No! How can students not be confused on the difference between research and statistical hypotheses when instructors are? *H*_{1} is not the research hypothesis, it is the alternative to the null hypothesis in a statistical test.

Let’s be very clear, in most research settings, there are two very distinct types of hypotheses: the Research or Experimental Hypothesis, and the Statistical Hypotheses. A research hypothesis is a statement of an expected or predicted relationship between two or more variables. It’s what the experimenter believes will happen in her research study. For example a researcher may hypothesize that prolonged exposure to loud noise will increase systolic blood pressure. In this instance the researcher predicts that exposure to prolonged noise (the independent variable) will increase systolic blood pressure (the dependent variable). This hypothesis sets the stage to design a study to collect empirical data to test its truth or falsity. From this research hypothesis we can imagine the scientist will, in some fashion, manipulate the amount of noise a person is exposed to and then take a measure of blood pressure. The choice of statistical test will depend upon the research design used, a very simple design may require only a *t* test, a more complex factorial design may require an analysis of variance, or if the design is correlational, a correlation coefficient may be used. Each of these statistical tests will possess different null and alternative hypotheses.

Regardless of the statistical test used, however, the test itself will not have a clue (if I am allowed to be anthropomorphic here) of where the measurement of the dependent variable came from or what it means. More years ago than I care to remember, C. Alan Boneau made this point very succinctly in an article in the American Psychologist (1961, 16, p.261): “The statistical test cares not whether a Social Desirability scale measures social desirability, or number of trials to extinction is an indicator of habit strength….Given unending piles of numbers from which to draw small samples, the *t* test and the *F* test will methodically decide for us whether the means of the piles are different.”

Rejecting a null hypothesis and accepting an alternative does not necessarily provide support for the research hypothesis that was tested. For example, a psychologist may predict an interaction of her variables and find that she rejects the null hypothesis for the interaction in an analysis of variance. But the alternative hypothesis for interaction in an ANOVA simply indicates that an interaction occurred, and there are many ways for such an interaction to occur. The observed interaction may not be the interaction that was predicted in the research hypothesis.

So please, make life simpler and more understandable for your students. Don’t call a statistical alternative hypothesis a research hypotheses. It is not. Your students will appreciate you making the distinction.

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For years I have been teaching social research (Null hypothesis) testing to doctoral and masters students where if rejected (or accepted), then the Alternate or Research hypothesis was supported (or not supported). I have yet to find confusion among my students with this methodology. I guess, I remain unconvinced how this is confusing the students as to how to use statistical tests to look for differences or correlations among dependent and independent variables.

For an excellent discussion of this, refer back Kinraide and Denison (2003, Am. Biol. Teacher, 65:419) and McPherson (2001, Am. Biol. Teacher, 63:242). Both of these papers are excellent and readily accessible for students and faculty. A statistical hypothesis is designed to describe patterns in data; a scientific hypothesis provide one or more candidate explanations that can be tested experimentally.