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Probability approximation

  1. May 23, 2013 #1


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    1. The problem statement, all variables and given/known data
    Take 50 free throws in basketball, and record the number of successful shots.

    1) Use this data to estimate your free throw percentage.
    2) Construct a 95% confidence interval for this estimated quantity.

    2. Relevant equations

    X ~ Binomial(n,p)

    3. The attempt at a solution

    I don't quite understand this. I would've guessed that given the data, my free throw percentage should be pretty obviously k/50 where k is the number of shots I landed. Maybe this means I should set the problem up as such?

    X ~ Binomial(50,k/50)

    But then it wants me to construct a confidence interval, so that can't be it, because I've taken the probability of landing a shot to be known.

    edit: Hang on, I think I need to assume they're normally distributed.
    Last edited: May 23, 2013
  2. jcsd
  3. May 23, 2013 #2

    Ray Vickson

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    If X = number of shots landed, X ~ Binomial(50,p) for some unknown constant p, and the success fraction is f = X/50. After observing X (or f) you want to infer something about the value of p. Normal distributions have not much to do with the problem, except maybe as an approximation to make computations easier. (Nowadays, with readily-available powerful software such approximations are less important than they used to be.)
  4. May 23, 2013 #3


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    What kind of inference for p would I make? I only have the data of shooting k successful throws out of 50 total, so all I can imagine doing with this is to falsely assume that my true average p is k/50.
  5. May 23, 2013 #4

    Ray Vickson

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    Nothing false about it as a probable approximation; you would not be surprised to find the true value of p being close to f= k/50.

    Let's cook up some simple numbers: say you observe f = 0.2, so k = 10. How surprised would you be by this result if you knew p = 0.9? Well, how probable is it that you would observe X as small as 10 (i.e., X <= 10) when X~Bin(50,0.9)? How surprised would you be to observe X as large as 10 when X~Bin(50, 0.001); that is, what would be the probability of observing X >= 10 in such a case? Note that we do not necessarily ask the probability that X = 10 exactly, since this will likely always be small for any p; it is just one of 51 separate probability figures that must all sum to 1.

    A confidence interval [a,b] is a kind of "not too surprised" interval, so that if p ε [a,b] the probability of observing X = 10 is not "too small". In your case you do not want to be more surprised than 5%, so you would typically (and usually) choose upper and lower limits giving 2.5% probability each. So, you would choose the upper limit 'b' to give P(X <= 10) = 0.025 for X~Bin(50,b) and the lower limit 'a' to give P(X >= 10) = 0.025 for X ~ Bin(50,a). Of course, we could choose upper and lower probabilities different from 0.025, just as long as the two of them sum to 0.05, but choosing the symmetrical case is customary.

    Modern software can handle such problems pretty easily, but you might, nevertheless, choose instead to use a normal approximation to the binomial.

    Formally: a 95% confidence interval [a,b] is a random interval whose chance of overlapping the true value of p is 95%. (The interval is random because it is constructed from the observed data, which is itself random.) Classical statisticians would say it is incorrect to speak of "the probability that p lies in the interval is 95%"; rather, they would insist that it is the interval itself to which the probability is attached. This concept can be tricky to grasp.

    A Bayesian (which I almost am) would find it a lot easier: he/she would start with a "prior" probability distribution f0(p) of the parameter, and would then just look at the posterior distribution f1(p) of p, after observing the data. That is, f1(r) = P{r < p < r + dr|X=k}/dr = P{X=k|p=r} * f0(r) /P{X=k}. For a uniform prior f0 = 1, we get
    [tex] f_1(r) = \frac{r^k (1-r)^{50-k}}{B(k+1,50-k+1)},[/tex]
    where ##B(u,v)## is the so=called Beta function:
    [tex] B(u,v) = \int_0^1 x^{u-1}(1-x)^{v-1} \, dx.[/tex]
    Anyway, p would now be regarded as having a probability density f1, and the confidence interval [a,b] would be truly an interval in which p was 95% likely to fall.

    The classical and Bayesian confidence intervals would be sightly different, since the classical method looks at {X <= 10} and {X >= 10}, while the Baseyian looks at {X = 10}.
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