1: Statistics: Benefits, Risks, and Measurements

Lesson 1 Overview Section

What is statistics? Statistics is about reasoning with data in the context of uncertainty in order to learn and understand things about the world around us. It is the science of learning from data.  We hope you enjoy exploring the topic as you move through the 11 lessons of the course.

Statistical Summaries and Pictures Parameters Samples STATISTICAL PARADIGM Population Probability The rules of probability tell us the likelihood of different types of samples that might arise from a particular population. Describe and Compare Data is collected from the samples and, with sample data in hand, we attempt to create statistical summaries and pictures that give the salient features of the data collected. Conclude What does our knowlwdge of the parameter values tell us about the population? Inference We want to infer what parameter values are most consistent with the sample statistic at hand.
Key Components of the Statistical Paradigm


Statistics is about how best to collect data to learn something valuable about the real world (from populations to samples: Lessons 1 to Lesson 3).  Statistics is about drawing out the salient features in the data collected (describing and comparing samples: Lesson 3 to Lesson 6).  Statistics is about learning some fundamental principles and procedures that will help you make intelligent decisions in everyday life when faced with uncertainty (understanding random chance for inference and making conclusions: Lesson 7 to Lesson 11).  We will revisit the figure above throughout this course to continually remind ourselves where we are in the "big picture" of the statistical paradigm.


After successfully completing this lesson, you should be able to:

  • Identify the three conditions needed to conduct a proper study.
  • Apply the seven pitfalls that can be encountered when asking questions in a survey.
  • Distinguish between measurement variables and categorical variables.
  • Distinguish between continuous variables and discrete variables for those that are measurement variables.
  • Distinguish between validity, reliability, and bias.