For anyone with a stats background:
If I have multiple results of a two-possibility event, how can I calculate the probability that the event was not a random 50-50 event (such as a coin toss).
For example, if there were four trials with possibilities A and B, and there were 3 A and 1 B, there's a good chance you are describing a coin flip, since it would not be very unusual to see 3 heads and 1 tail in four flips. But if there were 20 trials with 15 A and 5 B, despite being the same percentage A (75%), it's less likely that the event in question was a coin flip, although not incredibly unlikely. If there were 1000 trials and the results were 750 A, 250 B, it would be extremely unlikely that the event was a coin flip.
I'm looking to put a value on just how unlikely it is that the events are random in situations like these. Obviously this relates to betting in regards to knowing when a trend is probably revealing some causal link versus just statistical variance.
If I have multiple results of a two-possibility event, how can I calculate the probability that the event was not a random 50-50 event (such as a coin toss).
For example, if there were four trials with possibilities A and B, and there were 3 A and 1 B, there's a good chance you are describing a coin flip, since it would not be very unusual to see 3 heads and 1 tail in four flips. But if there were 20 trials with 15 A and 5 B, despite being the same percentage A (75%), it's less likely that the event in question was a coin flip, although not incredibly unlikely. If there were 1000 trials and the results were 750 A, 250 B, it would be extremely unlikely that the event was a coin flip.
I'm looking to put a value on just how unlikely it is that the events are random in situations like these. Obviously this relates to betting in regards to knowing when a trend is probably revealing some causal link versus just statistical variance.