I noticed that high range IQ tests with good quality approximately follow this logic:

   This table is a hint on how to make norms, but also a hint on how to be critical.
   We see that 50% change in solvability approximately makes a 10 IQ points change.

   What is a logical interpretation compared with my quality analysis?

   Simply, 50% quality is a 10 IQ points uncertainty. Your score is only in the +/- 10 points interval.
   As 0.7 squared is approximately 0.5, 70% quality is a 5 IQ points uncertainty.
   As 0.5 squared is 0.25, 30% quality is approximately 15-20 IQ points uncertainty.

   We can conclude that quality higher than 70% is desirable, while quality lower than 50% is almost useless.

   So, can we measure a high IQ?

   Yes, we can but with good tests and with certain uncertainty.
   Also, averaging scores from more tests is recommended.
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   In making my norms I combine the table above with the following table:

   I obtained these tables from the statistical data of Logima Strictica 36 as explained in the first post of my blog.
   For other tests those two tables are not in a perfect coincidence, but I always try to be somewhere between these two tables, giving a slight advantage to the second table.
   That's my way of avoiding both too generous norms and too strict norms.