Section 1 Matthew.pdf - Statistics 104 TF Matt DiSorbo 2015 2 Chapter 1 Qualitative Basis of Statistics Motivation As any empirical class worth its salt

# Section 1 Matthew.pdf - Statistics 104 TF Matt DiSorbo 2015...

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Statistics 104 TF: Matt DiSorbo 2015

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Chapter 1 Qualitative Basis of Statistics Motivation As any empirical class worth its salt must, Stat 104 has to establish proper methodology for its data collection and analysis. If you have taken a science class before, you know that this material is unapologetically dry; fortunately, our time with it in 104 is fleeting. Still, it is important to develop a strong understanding of these fundamentals, since they are the building blocks that the rest of the course (and all of Statistics) rest upon. What does this Chapter cover? Three sections: the relationship between population and sample and why it is so central to statistics, experimental design (experiments and observational studies, experimental fundamentals, types of observational studies) and common statistical pitfalls (lurking variables, sampling errors and bias). Section 1: The Population, Statistics Driving Force A large portion of Statistics exists because of the sheer, unavoidable fact that in our world it is usually difficult (if not impossible) to obtain information about every single object we are concerned about. This is a pretty bleak part of life, since it means that we can never really answer the question How much does a banana weigh on average?. Thats because, to answer this question exactly, you would need to gather every single banana in the world (this is called a census), and no one has the capacity needed to do that. Thankfully, Statistics comes to the rescue. Using tools that you will learn over the semester, it is possible to generate guesses of varying precision and even to diagnose just how wrong these guesses are. So, in this class, we will start with a population (call it all bananas), take a sample of said population (call it 10 bananas) and attempt to answer questions about the population as a whole based on our sample (how heavy are bananas on average?). 3

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4 CHAPTER 1. QUALITATIVE BASIS OF STATISTICS Section 2: The Toolbox for Statistical Studies The major two we will be dealing with are Observational Studies and Experiments. The focus on 104 is not on performing these studies, but on collecting the data from them and understanding their natures enough to make logical conclusions based on their results. So, while you will likely never have to conduct one of these yourself, they are still important to understand because we will be working with their results. The key distinction between an Observational Study and an Experiment is the word treatment. Treatments are imposed in Experiments and are not imposed in Observational Studies. These treatments are basically actions performed on groups in an experiment, as opposed to the passive observational studies where subjects are only observed from the sidelines. For example, consider testing if a new energy drink actually energizes the consumer. In an experiment, the treatment would be giving (treating) the drink to one group in the sample, while holding another group (the control group) constant (not giving them the drink).
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