Module Activity 3: Election Poll Simulation with Python
Reflection Question
1. In your own thoughts and opinion(s), do you trust polls? Why? Why not?
What are some of the strengths of an effective poll? Be sure to include concrete examples in your reflection
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2. Using your summaries in parts 3, 4, 5, and 6, create a cohesive reflection summarizing the Election Poll Simulation, the statistical concepts applied (Central Limit Theorem, Sample Size, Probability, Confidence Intervals, Margin of Error) and their usage in the context of this polling module and in real world election polls.
Reflection Answers
1. one can trust polls if they are accurately calculated and distributed. The polling sample size is very important for an effective poll. As the margin of error goes down the bigger the sample size, an effective pollster must be ready to spend the amount necessary to get as accurate a poll as possible. We certainly can't forget that those who are undecided voters, their vote would automatically be giving towards Donald Trump and this just proves that the polls cant be trusted, its just like a flip you just don't know what happens when you are an undecided voter. It doesn't sound promising and testing at all .
2. The Election Poll Simulation showed us how to work with data in many forms and without certain pieces of information. The Central Limit Theorem can be used to calculate margins of error for a sample of any size, check its significance, and increase or decrease the n(sample size)as much as the margin of error allows, thus giving us a mathematical way to check information on polls during elections when certain data is missing. We saw from our study that sample size plays a big role in accurate polling, and as the sample size gets bigger, we are able to see that the sample mean approximates a normal distribution. This can bring down margins of error and can help predict parameters of populations.