# Avoiding Statistical Fallacies

« Previous | Next »

## Session Overview

 This lecture discusses some common ways that people use statistics to draw invalid or misleading conclusions. Image courtesy of spweber on Flickr.

## Session Activities

### Lecture Videos

Topics covered: Statistics, plotting, correlation, causation, bias, logical fallacies, data enhancement, Texas sharpshooter fallacy.

### Recitation Videos

Topics covered: Dynamic programming, memoization, overlapping subproblems, optional substructure, Fibonacci memoization example.

## Check Yourself

What does GIGO stand for?

Garbage In Garbage Out: if your data is flawed, any conclusions you draw from it will be, too.

What does 'cum hoc ergo propter hoc' mean?

It is Latin for "with this, therefore because of this," a logical fallacy which involves incorrectly inferring a causation from a correlation. You should always remember—correlation does not imply causation.

What is a lurking variable?

One which doesn't appear in the plot of two other variables, but could have an effect on both. It is also sometimes referred to as a confounding variable.

## Problem Sets

### Problem Set 11: Fastest Way to Get Around MIT (Due)

In this problem set you will write a solution to an optimization problem on how to find the shortest route from one building to another on the MIT campus given that you wish to constrain the amount of time you will spend walking outdoors (because generally speaking, the nocturnal beaver… err, um, the nocturnal MIT engineer… hates the sun).

Note: Solutions are not available for this assignment.

## Further Study

These optional resources are provided for students that wish to explore this topic more fully.

« Previous | Next »