thanks a lot for your great effort,,,,is there any ideas else? or it is the only one?I think what they're trying to say is that there's a lot of variability not only with weighted percentage of each section, but also within the range of tthe Quartiles.
Student 1: Q2, Q2, Q3, Q3
Student 2: Q2, Q2, Q3, Q3
Perhaps Student 1 passes and Student 2 fails even though they have equivalent Quartile results because Student 1 was Top 51% for his Q3 results (top of Q3 Quartile) while Student 2 was Top 74% (bottom of Q3 Quartile but still in Q3). So if they are borderline to the cutoff, then Student 1 would have the statistical advantage over Student 2 in passing.
Hi @Elnur1 I'm baffled by the y-axis units of the displayed normal distribution; given the density, I believe the max y is 1/[sqrt(2*pi)*σ]; i.e., the pmf density function without the effectively discounting exp(). I don't see how the axis can get that high. I suspect it is merely visual. Please note GARP's methodology is based on quartiles (quantiles): there is no need to assume or impose normality. GARP's method does not impose normality, as far as I understand (it already quite sufficiently "grades on a curve"). The graphs may just be visual signals. And perhaps they varied the y-axis values because according to the topic weights. @Nicole Seaman If @Elnur1 really prefers, do you mind reaching out to confirm my assumption that "the normal graphs displaying along with the quartile reporting are merely visual indicators to convey quartile status. Specifically, the y-axis values are not exactly meaningful, nor do the pictorial normals imply any necessary normality; ie, normality would be coincident, as our understanding is that GARP does not actually impose normality on the score distribution." Thanks!