In correspondence with pertinent statistical tests, it is of practical importance to design data-sampling when the Rasch model is used for calibrating an achievement test. That is, determining the sample size according to a given type-I- and type-II-risk, and according to a certain effect of model misfit which is of practical relevance is of interest. However, pertinent Rasch model tests use chi-squared distributed test-statistics, whose degrees of freedom do not depend on the sample size or the number of testees, but only on the number of estimated parameters. We therefore suggest a new approach using an F-distributed statistic as applied within analysis of variance, where the sample size directly affects the degrees of freedom. The Rasch model’s quality of specific objective measurement is in accordance with no interaction effect in a specific analysis of variance design. In analogy to Andersen’s approach in his Likelihood-Ratio test, the testees must be divided into at least two groups according to some criterion suspected of causing differential item functioning (DIF). Then a three-way analysis of variance design (A>B)xC with mixed classification is the result: There is a (fixed) group factor A, a (random) factor B of testees within A, and a (fixed) factor C of items cross-classified with A>B; obviously the factor B is nested within A. Yet the data are dichotomous (a testee either solves an item or fails to solve it) and only one observation per cell exists. The latter is not assumed to do harm, though the design is a mixed classification. But the former suggests the need to perform a simulation study in order to test whether the type-I-risk holds for the AxC interaction F-test – this interaction effect corresponds to Rasch model’s specific objectivity. If so, the critical number of testees is of interest for fulfilling the pertinent precision parameters. The simulation study (100 000 runs for each of several special cases) proved that the nominal type-I-risk holds as long as there is no significant group effect. Analysing a certain DIF, this F-test has fair power, consistently higher than Andersen’s test. Hence, we advise researchers to apply our approach as long as there is no significant group effect, and only to use other Rasch model tests if it is significant. Keep in mind that this is true only for some special cases and needs to be generalized in further research. Then a formula needs to be provided which will allow explicit calculation of the number of testees, given a type-I-, a type-II-risk, and a relevant effect as concerns Rasch model misfit.