Learn about the role of statistical inference; population and sample; selection effects; and sampling variation.
- [Instructor] Statistical inference describes the process of drawing conclusions from data and of understanding the confidence we may have in those conclusions. In chapter three we discussed many ways to describe the variation in variables using both descriptive statistics and many kinds of plots. In this chapter we worry about the uncertainty in our statements about variation. The uncertainty arises because we can only study a limited sample of the population that we really want to find about.
For instance, we may wonder about all the extra solar planets in our galaxy. But we can only compute statistics for those that we have observed through telescopes. In this chapter I will show you how simple mathematics and computer simulations can be used to understand the randomness due to limited samples. A separate issue is whether the sample is representative of a population. For instance, it is much easier to detect large extra solar planets, so large planets would be over-represented in our sample and skew the resulting population statistics.
This selection effects can be addressed mathematically or by improving our data taking. Both topics are outside the scope of this course, but you can find lots of about them in statistics text books, so let's get started.
- Installing and setting up Python
- Importing and cleaning data
- Visualizing data
- Describing distributions and categorical variables
- Using basic statistical inference and modeling techniques
- Bayesian inference
Skill Level Intermediate
R Statistics Essential Trainingwith Barton Poulson5h 59m Intermediate
SPSS Statistics Essential Trainingwith Barton Poulson4h 57m Beginner
1. Installation and Setup
2. Importing and Cleaning Data
3. Visualizing and Describing Data
4. Introduction to Statistical Inference
5. Introduction to Statistical Modeling
Next steps1m 55s
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