Response to discussion posts

Answer these 3 discussion posts like you are talking to someone. Each response has to be between 150-250 words and you have to agree or disagree with his statements. First discussion post: I typically expect to see more context before immediately trusting statistics I see in print. I remember when I was taking stats all the way back in high school and I saw a chart citing almost a direct correlation between ice cream sales and shark attacks. As many people have heard before we know that correlation does not equal causation. I realized there is usually some additional context that should be considered; In this case the context is likely the seasonality of both ice cream sales and people swimming in the ocean. To take this even further I oftentimes see advertisements claiming “4 out of 5 doctors recommend this product.” As mentioned above context needs to be considered. Companies using these statistics can certainly be misleading as it is unlikely that 80% of doctors recommend one product over another. To determine if one should believe reported statistics I believe it is important to consider the source and context of all claims. Instead of blindly trusting claims perhaps it is a good idea to open the source and read the “study” provided and focus on who conducted the study. While statistics can be very helpful it is important that individuals realize they can easily be manipulated and additional information is oftentimes needed. Second discussion post: Typically as a trusting type of person I will usually trust and believe statistics that are given in reputable news stories or reports. After the first week of this course though I am realizing that it would be much smarter to understand how the statistic has been collected analyzed computed and displayed. Is the statistic computed from a large or small sample? How was the data collected? What questions were asked to illicit a response? If answers were only given as multiple choice what were the options to choose from? Who took the survey? Also when reading a chart or table knowing the scale or the options given is important to understanding how the data is graphed and represented. There are easy methods that can be used to make a graph or chart misleading by not using consistent groupings or scale. If a product sales pitch says that most women prefer red cars. What does “most” mean to the reader of the sales pitch? Most could be interpreted as almost 100%. So some people might believe that almost all women would like to own a red car. However the word “most” really could just be “more than 50%.” And what if the question that caused the response to women liking red cars was “Which do you prefer more red cars or purple cars?” Since there are only two options with one of them being an unlikely option the red car is going to be the more preferred car. This is misleading. Statistics can be very helpful but one should not be trusting of the results without understanding how the statistic was calculated. Third discussion post: Working in sports statistics can be very straight forward. If you look at the box score of a football game you can see that a quarterback threw 32 passes completed 20 of them for 324 yards and of those 20 passes three of them were for touchdowns. Sometimes stats can be uncredible when looking at the total outcome of the game. For example Missouri defeated Florida in football in 2014 by a score of 42-13 despite the Tigers only passing for 20 total yards and rushing for just 99. However the Gators committed four turnovers two of which were returned for touchdowns. The whole box score must be read not just the “pretty” or “more important” stats. Teams have also become more worried about “analytics” instead of actually just playing the games. Just because the probability says you should kick a field goal that is worth only three points instead of going for a touchdown that is worth six points doesnt mean you should. What if the defense has been carved up by the opposing offense all day? What if the kicker is notorious for missing kicks late in games? To fully trust sports stats at least one needs to look at the whole game and maybe even whole seasons for certain coaches and players before making judgements on if a player or coach is elite. Nick Saban lost to UL Monroe in his first year at Alabama while Dabo Swinney went 6-7 and lost to South Florida in his third year. Now they are both the most winningest college football coaches at their respective schools with multiple national championships. Context is king and for credible sports statistics one must look at the whole picture. One bad game or season does not ruin a team coach or player. References Florida Gators. (2014 October 18). https://floridagators.com/sports/football/stats/2014/missouri/boxscore/2790 (Links to an external site.). Requirements: 150-250 words for each response