Statistics
Lessons in this chapter
About Statistics
Statistics closes the course by covering how data is collected, distributed, and interpreted with rigor.
Introduction to Normal Distributions and its Properties and Standard Normal Distributions cover the bell-curve distribution and how to standardize it for comparison across different datasets.
Sampling and Observational Studies covers how data is actually collected, distinguishing between a designed sample and an observational study.
Estimating Population Parameters and Drawing Conclusions using Data from an Experiment close the chapter by using sample data to estimate a whole population’s characteristics and to decide what a real experiment’s results actually support.
All five lessons, from Introduction to Normal Distributions and its Properties through Sampling and Observational Studies to Drawing Conclusions using Data from an Experiment, open as worksheets worked in the page. The practice questions are scored as you answer them. Free to try, with no login needed for your first 3.
Frequently asked questions
Introduction to Normal Distributions and its Properties covers the bell curve and the share of data lying within one, two, and three standard deviations of the mean. Standard Normal Distributions converts any normal distribution to z-scores so values can be compared, Sampling and Observational Studies separates surveys, observational studies, and experiments, Estimating Population Parameters uses a sample to say something about a whole population, and Drawing Conclusions using Data from an Experiment judges whether a difference is real. This sits in the Statistics and Probability (S) conceptual category of the high school Common Core.
A normal distribution is a symmetric, bell-shaped distribution described completely by its mean and standard deviation.
The standard normal distribution has a mean of 0 and standard deviation of 1, and any normal distribution can be converted to it (standardized) using a z-score, allowing comparison across different datasets.
A sample studies a subset chosen from a larger population, while an observational study observes existing conditions without imposing a treatment: the distinction affects what conclusions can validly be drawn.
Random sampling gives every member of a population a fair chance of selection, reducing bias so the sample better represents the whole population.
A confidence interval gives a range of plausible values for a population parameter, built from a sample statistic and a margin of error.