Last week I gave a colloquium at MPIA in which I advocated the use of jackknife and bootstrap resampling to obtain empirical uncertainty estimates in a complex data analysis. Today I actually implemented jackknife in my project on cosmic homogeneity (and isotropy). I jackknifed by sky position: I split the sky into 12 nearly-equal regions for 12-fold leave-one-out. I have intuitions about when it is a good idea to jackknife on a quantity (like sky position) and when it is a good idea to jackknife on a quantity that is completely random, but I don't know exactly where my intuition comes from. In general it must be the case that jackknifing on different things answers different questions about your noise.
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