Timeline for Statistical significance of ordered binary vectors
Current License: CC BY-SA 3.0
9 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Mar 10, 2013 at 1:45 | vote | accept | Makoto | ||
| Mar 8, 2013 at 7:05 | answer | added | Makoto | timeline score: 1 | |
| Mar 8, 2013 at 4:03 | comment | added | Makoto | I am predicting where people will go at intersections, so there are an average of 3 branches, hence .33 chance accuracy. However, each prediction is correct or incorrect, hence the binary. | |
| Mar 8, 2013 at 2:56 | comment | added | David Robinson |
Much easier, but let's focus on the .33 number. you said these are binary predictions (patient mortality?). You must at least be able to hit 50% by random chance alone
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| Mar 8, 2013 at 2:09 | history | edited | Makoto | CC BY-SA 3.0 |
added 182 characters in body
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| Mar 8, 2013 at 2:08 | comment | added | Makoto | Yes, I just want to see if my predictions are greater than chance. Random predictions, averaged from 100 trials, are 0.33 accurate. Is there an easier way to test significance? | |
| Mar 7, 2013 at 15:30 | review | First posts | |||
| Mar 7, 2013 at 15:40 | |||||
| Mar 7, 2013 at 15:20 | comment | added | David Robinson | Unless I'm mistaken, it sounds like you are greatly overcomplicating this. Do you just want to see if the prediction rate is significantly higher than expected by random chance? (note that the prediction rate by chance is not necessarily 50%. If 25% of patients die, and your algorithm predicted that all of them would live, your algorithm would have a 75% accuracy rate). | |
| Mar 7, 2013 at 15:14 | history | asked | Makoto | CC BY-SA 3.0 |