Statistical formulas

For that we preserve only the samples for which we can determine the category without too much of risk to be mistaken, i.e. those for which:

• the difference between the majority category and the following one is equal to or higher than 2 and the person does not believe to be in another category. OR
• the difference between the majority category and the following one is equal to or higher than 1 and the person do believe to be in the majority category.

We assign 1 to the selected answers and 0 to the nonselected ones (table top). For each answer, we calculate the averages by categories (µ _v, µ_a, µ_k). We deduces (v, a, k) by applying a coefficient such as v + a + k = 100 (formula opposite).

Three tables of 30 elements are thus obtained: v [30], a [30], k [30].

We check in the passing that they are negatively correlated one from each other . For our sample we find: þ_vk = -0.60, þ_va = -0.60, þ_ak = -0.48.

We can then calculate the coefficient of correlation þ between each one of these tables and the table of your answers r [30]. It is the formula opposite with the huge square roots.

A question remain open:

Do the experimental values of þ _vk , þ_va and þ_ak obtained by this method constitute an evidence of the validity of these three categories, or would we have found results comparable with questions based on whimsical categories such as the astrological sign?

In own way of brief reply, I delivered myself to the same calculation starting from a set of 50 samples generated by chance. (I could employ to classify them only the first of the two criteria described above), and I found the values þ_vk= -0.46, þ_va= -0.51, þ_ak= -0.48. The fact that these results are more distant from -1 than the results drawn from the real samples tends to confirm, I think, the validity of these categories.

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