![]() ![]() This shows that what the statisticians have really in mind in problems of estimation is not the idea of a unique estimate but that of two estimates of the form, say $$\underline\theta = T - k_1S_t \quad \text \quad \overline\theta = T k_2S_t $$ ![]() In that sense it contains more information and is more meaningfull.Īn interesting argument has been made by Neyman (in "Outline of a Theory of Statistical Estimation Based on the Classical Theory of Probability") while formulating confidence intervals he wrote something that estimation is not just about point estimates, but instead about ranges. It is not only pointing out the region/point where the parameter is most likely estimated, it also provides information about the accuracy of this estimate. You could view an interval estimate as providing extra information about the estimate. My question is what makes interval estimates more meaningful for estimation than point estimates? The point estimate and interval estimates serve different purposes. They are not compared in a sense like advantages and disadvantages for a particular problem and based on that the one estimate is selected instead of the other. Point estimates and interval estimates are not really be substitutes for eachother. What's the advantage of a point estimate over an interval estimate? ![]() Will this ice-cream producer place his order using an interval "I would like to buy between 12 kg sugar, please" or will the ice-cream producer use some fixed number "I would like to buy 1225 kg sugar, please"? He can decide on the quantity by using some estimates for the number of ice-creams that he will be selling, which relates to the amount of ingredients he needs. Often the point estimate is the primary goal and the interval (or error estimate) is auxilliary meta-information which tells us something about the point estimate (the accuracy/uncertainty).Īn ice-cream producer needs to place an order for some amount of sugar. ![]()
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