Quote:
Originally Posted by Robert Tulip
John – I am not cherry-picking. Moon cycles with Sun, Venus, Saturn, Uranus and Neptune all returned results outside that produced by random numbers. I am not just using the probability of the most significant result in isolation, but am dividing it by 29 to recognize there are 29 samples, and then comparing this against other tests.
The results are independent. The two most significant results are different dates on the Moon-Uranus cycle, and these have no data points in common. The same applies for days 6 and 7 of the Sun-Moon cycle. Only four of the sixty rainiest days were on the significant Moon-Uranus -10 dates. These four dates only accounted for 1.97% (653mm) of total rain in the sample, proving that the same outliers are not actually responsible for multiple effects.
My multiplication of probabilities is valid. By comparison, if a result is seen in 10% of normally distributed samples, and one test produces two such results, this combined result has probability of 1%. In my test, I applied this method to assess the probability of both the Moon-Uranus peak (seen in 3.65% of random samples) and the Moon-Uranus trough (seen in 18.8% of random samples) to indicate a probability of 0.68% (~1/150) that both results would occur in one random sample.
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Robert, your results are
NOT independent. They are different analyses of the
same dataset, so the
same outlier values may be causing the "anomalous" results in your different analyses. Your "effects" are also going to be correlated - the Moon is a common factor in all of them. And because they're not independent, you can't just multiply probabilities together. The only way you can look at multple effects is to do a multivariate analysis.
Quote:
Originally Posted by Robert Tulip
I note that no one has asked to see my data. I assure you it is accurate, but of course looking at it would give credence to a new scientific theory which is against the mainstream.
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I haven't asked for your data, simply because I don't have time to look at them in detail right now. If I have time later, I'll let you know.
Quote:
Originally Posted by Robert Tulip
There is no reason other than a planetary effect why this data would not be normally distributed.
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I'm still not convinced that the averages should be normally distributed. As I've suggested before, there is likely to be a long right-hand tail to the distributions. Plus, as aurora pointed out, because of the duration of weather patterns, your data are not independent. Have you tested your assumption of normality? How?
Quote:
Originally Posted by Robert Tulip
For comparison, I replaced the planetary data with random numbers and over several runs as expected found all 29 results had Standard deviation less than 2. It would take 150 such random samples to see both the Moon-Uranus results observed in the Sydney rain data.
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How were your random numbers distributed? If their distribution is not similar to your data, this comparison is worthless.