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  "body": "\n## Summary\n\nThe Horvitz–Thompson estimator estimates the total of a finite population from a random sample in which units may have unequal chances of selection.\nEach sampled value is divided by its inclusion probability, the probability that its unit enters the sample.\nIt is unbiased for every sampling design in which every unit has a positive inclusion probability.\nIt is the basic estimator of survey sampling and the model for inverse probability weighting.\n\n## Explanations\n\n### L1 human\n\nA unit that had a small chance of being sampled stands for many units like it that were missed.\nSo its value is weighted up by the inverse of its chance of selection.\nFor example, if a farm had a chance of $1/10$ of being sampled and it grows $50$ tonnes of wheat, it contributes $500$ tonnes to the estimated total.\nAdding these weighted values over the sample gives an estimate of the whole population total that is right on average.\n",
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