Yes. Ask her to carefully record the suits as well as the denominations of the cards involved.
Okay. If she does, I'll start a new thread though.
And that number is incorrect. His correct number of expected wins is 16.8, or 16.8/29 = 57.9%.
I wouldn't call it incorrect, but simply a less precise estimate of the expectation. It's quite reasonable to sacrifice precision for convenience in informal situations like this. I computed it because I wanted to compare my hubby's results to LogicFail's, and I needed to use the same formulation to make a comparison.
LogicFail's actual result was close to his expected result.
Nonsense? There is no reason to think that the actual number of hands he won should be close to the number of hands that he had a probability greater than .5 of winning.
While it isn't a very precise estimate, I don't think it will introduce bias so the accuracy should be unaffected. It might be interesting to run a simulation and see how it performs. Or it could be formalized and the potential bias computed. Hmmm...that might be an interesting exercise.
At any rate, I'm not willing to dismiss the results preemptively. Statistical precision and formality are not always required for people to make good use of data. As I said, I generally like to encourage people to collect data and analyze it in order to make improvements. But then, I have a long background of working in quality improvement. A little data can go a long way towards helping people make improvements in whatever they are doing.
My experience in applying statistical quality improvement techniques in manufacturing situations was that the simpler the data collection scheme, the more likely the data was to actually be collected correctly. The simpler the analysis used, the more likely the result was to be acted upon.
Complex and precise data collection and analysis may be necessary for scientific papers, but it isn't required for effective qualify improvement. Very simple techniques, as LogicFail used here, were often the most powerful for that situation because they were more likely to be used by the general population.
His true expected number of wins could have been as high as 209 or as low as 90, so his calculation does not even indicate whether he's done better or worse than expectation.
I'm not sure how you are computing the bounds of 209 and 90 for the expected value in this situation. Could you explain?