A Describe On the Understanding of the Bayesian Flip
The Bayesian flip looks at the probability of an event after gaining new data based on previously known information about the event. As such, it flips the approach from depending on data to get the theory to using theory to explain the probability of data. It further interrogates the effect of inaccuracy in the probability of accepting a given hypothesis. For instance, if a certain event such as raining has a 40% chance of happening in town A and another town B has a 70% chance of happening. The Bayesian flip provides the conditional probability of telling whether data obtained from a study is from town A or B. Thus, assuming there is a 50% chance of choosing between town A and B; The chance of it raining in will the sum of it raining in A plus B. For A, multiply the 50% chance of choosing either town A or B by 40% (probability of raining in town A) giving 20%. For the probability of it raining in B, multiply the 50% of choosing either town A or B multiplied by 70% ( probability of raining in town B) giving 35%. So, the sum (20%+35% = 55%) gives the probability of raining in either A or B. Therefore, when new data is collected and a rainy day is selected there will be 36% (20%/55% ≈ 36%) the data will be from town A. Town B, on the other hand, will have 63% (35%/55% ≈ 63%) chance.
An Opinion about the Innocence or Guilt of Troy Brown and How Would 1 In 3 Million Compare To 1 In 333.333
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The basing of Troy Brown's guilt on circumstantial evidence coupled with frequentist interpretation is misleading. It is crucial to interrogate the innocence or guilt of Troy Brown using approaches such as the Bayesian statistics. The use of Bayesian statistics interrogates the variation of chances of him being guilty from 1 in 3 million compare to 1 in 333.333. Such a huge shift prompts the prosecution to find more concrete evidence if they are to prove that Troy Brown is guilty.
Whether The Mammograms High False-Positive Rates Is A Surprise
It is a surprise that mammograms have such high false-positive rates. The Bayesian flip allows one to interrogate the 3.6% accuracy that results in a 96% false positive. As such, the test calls for secondary tests to establish the accuracy of the results.
Whether OJ Simpson's Defense Attorneys Tricked the Jury with Their Statistical Interpretation of Abuse vs. Murder
I don’t think that OJ Simpson's defense attorneys tricked the jury with their statistical interpretation of abuse vs. murder. The fact that the statistical interpretation can be misinterpreted means that there is reason to doubt it. As such, even if they presented an irrelevant probability, the defense attorneys proved how statistics can be misinterpreted to wrongfully deem a suspect guilty.
