Many college entrance exams in the early s were specific to each school and required candidates to travel to the school to take the tests.
Please read that instead. It looks like this random statistics thing. So you came here. Why does a mathematical concept generate this strange enthusiasm in its students? What is the so-called Bayesian Revolution now sweeping through the sciences, which claims to subsume even the experimental method itself as a special case?
What is the secret that the adherents of Bayes know? What is the light that they have seen? Soon you will know. Soon you will be one of us. Bayesian reasoning is very counterintuitive.
People do not employ Bayesian reasoning intuitively, find it very difficult to learn Bayesian reasoning when tutored, and rapidly forget Bayesian methods once the tutoring is over. This holds equally true for novice students and highly trained professionals in a field. Bayesian reasoning is apparently one of those things which, like quantum mechanics or the Wason Selection Test, is inherently difficult for humans to grasp with our built-in mental faculties.
Or so they claim. Here you will find an attempt to offer an intuitive explanation of Bayesian reasoning - an excruciatingly gentle introduction that invokes all the human ways of grasping numbers, from natural frequencies to spatial visualization.
The intent is to convey, not abstract rules for manipulating numbers, but what the numbers mean, and why the rules are what they are and cannot possibly be anything else.
When you are finished reading this page, you will see Bayesian problems in your dreams. A woman in this age group had a positive mammography in a routine screening. What is the probability that she actually has breast cancer? What do you think the answer is?
Is that a real number, or an urban legend based on an Internet poll? Do you want to think about your answer again? This calculator has the usual precedence rules; multiplication before addition and so on. If women in this age group undergo a routine screening, about what fraction of women with positive mammographies will actually have breast cancer?
If 10, women in this age group undergo a routine screening, about what fraction of women with positive mammographies will actually have breast cancer?
The correct answer is 7. Out of 10, women, have breast cancer; 80 of those have positive mammographies. From the same 10, women, 9, will not have breast cancer and of those 9, women, will also get positive mammographies.
Of those 1, women with positive mammographies, 80 will have cancer. To put it another way, before the mammography screening, the 10, women can be divided into two groups: Summing these two groups gives a total of 10, patients, confirming that none have been lost in the math.
After the mammography, the women can be divided into four groups: As you can check, the sum of all four groups is still 10, The sum of groups A and B, the groups with breast cancer, corresponds to group 1; and the sum of groups C and D, the groups without breast cancer, corresponds to group 2; so administering a mammography does not actually change the number of women with breast cancer.
If you administer a mammography to 10, patients, then out of the with positive mammographies, 80 of those positive-mammography patients will have cancer. This is the correct answer, the answer a doctor should give a positive-mammography patient if she asks about the chance she has breast cancer; if thirteen patients ask this question, roughly 1 out of those 13 will have cancer.
The most common mistake is to ignore the original fraction of women with breast cancer, and the fraction of women without breast cancer who receive false positives, and focus only on the fraction of women with breast cancer who get positive results.
Figuring out the final answer always requires all three pieces of information - the percentage of women with breast cancer, the percentage of women without breast cancer who receive false positives, and the percentage of women with breast cancer who receive correct positives.
To see that the final answer always depends on the original fraction of women with breast cancer, consider an alternate universe in which only one woman out of a million has breast cancer. If you administer mammographies to ten million women in this world, around eight million women with breast cancer will get correct positive results, while one woman without breast cancer will get false positive results.
Thus, if you got a positive mammography in this alternate universe, your chance of having cancer would go from How to Write an Essay Introduction.
In this Article: Article Summary Sample Essay Hooks & Introductions Hooking Your Reader Creating Your Context Presenting Your Thesis Bringing It All Together Community Q&A The introduction of your essay serves two important purposes.
First, it gets your reader interested in the topic and encourages them to read what you have to say about it. The correct answer is %, obtained as follows: Out of 10, women, have breast cancer; 80 of those have positive mammographies.
From the same 10, women, 9, will not have breast cancer and of those 9, women, will also get positive mammographies.
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