I don't like posting anything that's wrong so i checked...#1 is quite funny.
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...and it re-wrote it for us. I might get it to do all my posts, it's faster than I am
When your sample size isn't large, you might still consider the Central Limit Theorem (CLT) to understand if your sample is representative of what's expected. The CLT deals with the distribution of sample means — for example, you might look at a histogram of your data points. This can help you determine if current variations are due to outliers or just short-term randomness.
Creating a sample distribution (the frequency of certain ranges of wins and losses) should reveal a pattern that resembles a bell curve, albeit not perfectly. Notable deviations from this pattern could indicate unusual results, but remember, this is not an endorsement of the Gambler's Fallacy. Prior results don't affect future outcomes; the universe doesn't compensate for past losses or wins. However, over a large number of trials, the Law of Large Numbers (LLN) assures that your results will align with the expected probability distribution.
Please note that a large enough sample size is crucial for the CLT to be applicable, and in practice, 'large enough' can vary depending on the actual distribution of your data.
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