> It's suspicious when it lands on something that people might be biased towards.

Eh, this only makes sense if you're incorporating information about who set up the experiment in your statistical model. If you somehow knew that there's a 50% probability that you were given a fair coin and a 50% probability that you were given an unfair coin that lands on the opposite side of its previous flip 90% of the time, then yes, you could incorporate this sort of knowledge into your analysis of your single trial of 200 flips.

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wat10000 8 hours ago | parent [-]

If you don’t have any notion of how likely the coin is to be biased or how it might be biased then you just can’t do the analysis at all.

	▲	tshaddox 2 hours ago \| parent [-]
		You can certainly do the frequentist analysis without any regard to the distribution of coins from which your coin was sampled. I’m not well studied on this stuff, but I believe the typical frequentist calculation would give the same results as the typical Bayesian analysis with a uniform prior distribution on “probability of each flip being heads.”