Download Probability

Probability:
Two approaches to interpret probability include the frequentist approach or the Bayesian approach. To
be perfectly honest before writing this discussion post I had no clue what these two statistical methods
were referring to. I now know that the frequentist approach focuses on the probability of the data given
the hypothesis, and The Bayesian approach is the opposite. This method focuses on the probability of
the hypothesis given the data (Fox, 2011). After reading about both approaches one thing is certain,
both the frequentist and Bayesian probabilities must satisfy the same algebraic rules to be statistically
valid (Ambaum, 2012). I can view both sides. I agree with the frequentist logic that “the more data we
collect, the better we can pinpoint the truth (Ambaum, 2012).” I also agree with the Bayesian approach
which allows previous beliefs to be interpreted when observing data. Overall, I feel I am not familiar
enough with both approaches to make a stance on which interpretation would be superior over the
other. I must admit though it is pretty amusing reading people get so worked up over which method
reigns supreme.
In the blog we read this week the author says “a lot of people frequently misunderstand how to apply
statistics: they’ll take a study showing that, say, 10 out of 100 smokers will develop cancer, and assume
that it means that for a specific smoker, there’s a 10% chance that they’ll develop cancer. That’s not true
(Chu-Carroll, 2008).” I agree with the author. Just because there is a 10% chance that 10 out of 100
smokers will develop cancer does not mean that the smoker will indeed get cancer. There is never a sure
way of knowing.
I took statistics a decade ago, so my memories are vague. I do remember attempting to learn about “p
values” and “z-scores” in a noisy lecture hall at Clemson University. Learning biostatistics from home is
a nice change of pace. It is pleasant to have a quiet place to focus, because sometimes I have a difficult
time setting up probability problems. If I am not careful I can get confused and read the question wrong.
Needless to say if you set the arithmetic up wrong then the probabilities you are trying to calculate will
be wrong. I think Benoit Mandelbrot describes probability best when he said, “The theory of probability
is the only mathematical tool available to help map the unknown and the uncontrollable. It is fortunate
that this tool, while tricky, is extraordinarily powerful and convenient.”
References
Chu-Carroll, M. (2008, April 7). Schools of thought in Probability Theory. Retrieved from Science Blogs:
http://scienceblogs.com/goodmath/2008/04/07/schools-of-thought-in-probabil/
Fox, J. (2011, October 11). Frequentist vs. Bayesian . Retrieved from Oikos:
http://oikosjournal.wordpress.com/2011/10/11/frequentist-vs-bayesian-statistics-resources-to-helpyou-choose/
Ambaum, M. (2012). Frequentist vs Bayesian statistics—a non-statisticians view Retrieved
from: http://www.met.reading.ac.uk/~sws97mha/Publications/Bayesvsfreq.pdf