?I need help with my statistics assignmetn. Please see attachment for directions!

Please pick one of the links and talk about what you learned. This is an example from a studet
? The birthday article caught my attention. Basically the probability of someone having the
same birthday can be as high as 1 in 2 when the number of people is 23. What happens
when a popular television host Johnny Carson asks the audience if someone has the same
birthday as the lady sitting in the front row and nobody answer? Then we think this is a
math error. However, the question was worded wrong. He mentioned a specific date
meaning his sample needs to be bigger. Instead, the number 23 works only when any
person have the same birthday. When we try to explain statistics, wording matters. In
addition, the bigger the sample we can obtain we will get a more accurate conclusion. We
can use a smaller sample; let?s say 40 people as long as we don?t have outliers. Again,
statistics have infinite uses you can even find the probabilities of finding love? Yes, LOVE
or at least your perfect match.
References
Blitzstein, J. Statistics, hints & love. Retrieved April 5, 2016, from
http://www.stat.harvard.edu/Academics/stat_hints_love.html?
There is an overabundance of help on the Internet. I recommend Khan, as always, and I like my
usual favories (I like familiar, not so happy with change, maybe because I'm old) of stattrek,
easycalculation, and mathisfun. My favorite interactive calculator is Interactive Normal
Distribution. Check it out.
A quick and dirty explanation of CLT can be found in stattrek and states:

Central Limit Theorem
The central limit theorem states that the sampling distribution of any statistic will be normal or
nearly normal, if the sample size is large enough. Generally, a sample size is considered "large
enough" if any of the following conditions apply.

The population distribution is normal.

The sample distribution is roughly symmetric, unimodal, without outliers, and the sample
size is 15 or less.

The sample distribution is moderately skewed, unimodal, without outliers, and sample
size is between 16 and 40.

The sample size is greater than 40, without outliers.

I also like Normal Curve Simulator
http://davidmlane.com/hyperstat/z_table.html

Here's a real life example of the normal distribution from a recent NYTimes article:
The Most Economically Diverse Top Colleges
http://www.nytimes.com/interactive/2014/09/09/upshot/09up-college-access-index.html?
abt=0002&abg=1&_r=0

and here's something just for fun:
Birthdays
I like Steven Strogatz' occasional articles in the New York Times. Check our his blog if you
enjoy this kind of thing. In your spare time, of course.
Normal as rabbits' weights and dragons' wings
Here are two additional sites recommended by students.
https://www.khanacademy.org/math/probability/statisticsinferential/sampling_distribution/v/central-limit-theorem
http://spin.atomicobject.com/2015/02/12/central-limit-theorem-intro/
Hypothesis testing for beginners