Statistics 100 Fall 2017 Sections

Karle Flanagan
Section L1
TR 12:30pm-1:50pm
Lincoln Hall Theater

Section L2
TR 3:30pm-4:50pm
Lincoln Hall Theater

Ellen Fireman
Online Section

Danielle Sass
Section S1
MWF 12:00pm-12:50pm
1027 Lincoln Hall

Outline of Course Content

Experimental Design - Why randomized controls are key.
What the possible confounders in observational studies are.

Descriptive Statistics - mean, median, SD, histograms, normal curve, etc.

Linear Regression - correlation coefficient, regression equation, etc.


Statistics for Chance Numbers - expected value and Standard error of chance processes, probability histograms and convergence to normal curve. Focus is on developing simple chance models box models- drawing numbers at random from a box) that more complicated sampling processes can be translated into.

Sampling and Statistical Inference - Using sample means and percents to estimate population means and proportions, and attaching margins of errors to our estimates by computing confidence intervals. Why randomized sampling is key.

Hypothesis Tests-one sample and two sample Z-tests, t-tests and chi-square tests for goodness of fit and independence. Focus is on understanding how these tests depend on chance models.

Limits of Significance Tests- understanding what the P-value means and under what circumstances it is valid. (For example, hypotheses must be stated before looking at the data, the total number of experiments before significant results were found must be reported, etc.)

Why everyone needs to know basic statistics:

Statistics is a tool to make sense of large amounts of information. Common sense can only handle limited amounts of information. Until recently common sense was sufficient for most people because daily life didn't involve processing a large amount of data. But in the past 30 years or so, with the advent of personal computers, large stores of information have become readily available. You can either choose to ignore the information available or you can choose to make sense of it, which means learning statistics.

Why most people think statistics is boring or worse:

Most people think statistics is boring for a good reason--it's not about anything! Art is about beauty, science is about nature, history is about people... and statistics is about nothing. It's just a tool, but it's such a difficult tool for most people to learn how to use that it becomes worse than boring. It becomes tedious, confusing and frustrating.

Why Stat 100 is not too boring or frustrating:

Statistics is to data, what grammar is to words. And like grammar, it's only interesting if it's used to understand something interesting. In Stat 100, we use statistics to research a topic we're all interested in--ourselves. We'll collect data on ourselves through anonymous surveys. If we can come up with interesting questions that we can only answer through learning statistics, the process will be less painful and more productive.

Students tell me that after Stat 100 they:

  • Read the newspaper in a new way, without their eyes glazing over when they see quantitative information.
  • Know what questions to ask in evaluating studies and surveys.
  • Understand what questions can and cannot be answered by statistical arguments.
  • Appreciate how much of what matters to them can be better understood with statistics.
  • Feel much more confident applying both logical reasoning and common sense to quantitative topics but are very aware that their intuition can sometimes be so wrong that it's shocking.
  • But what's most surprising is they ACTUALLY LIKE STATISTICS!!!!!!
  • Meditations on the Statistical Method

    Plato despair!
    We prove by norms
    How numbers bear
    Empiric forms,

    How random wrongs
    Will average right
    If time be long
    And error slight;

    But in our hearts
    Curves and departs
    To infinity.

    Error is boundless.
    Nor hope nor doubt,
    Though both be groundless,
    Will average out.

    JV Cunningham

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