This first chapter begins by discussing what statistics are and why the study of statistics is important. Subsequent sections cover a variety of topics all basic to the study of statistics. One theme common to all of these sections is that they cover concepts and ideas important for other chapters in the book.
- 1.4.1: What are Statistics?
- Statistics include numerical facts and figures, but also involves math and relies upon calculations of numbers. It also relies heavily on how the numbers are chosen and how the statistics are interpreted.
- 1.4.2: Importance of Statistics
- It is important to properly evaluate data and claims that bombard us every day. If you cannot distinguish good from faulty reasoning, then you are vulnerable to manipulation and to decisions that are not in your best interest. Statistics provides tools that you need in order to react intelligently to information you hear or read. In this sense, statistics is one of the most important things that you can study.
- 1.4.3: Descriptive Statistics
- Descriptive statistics are numbers that are used to summarize and describe data. The word "data" refers to the information that has been collected from an experiment, a survey, a historical record, etc. Descriptive statistics are just descriptive. They do not involve generalizing beyond the data at hand. Generalizing from our data to another set of cases is the business of inferential statistics.
- 1.4.4: Inferential Statistics
- In statistics, we often rely on a sample --- that is, a small subset of a larger set of data --- to draw inferences about the larger set. The larger set is known as the population from which the sample is drawn.
- 1.4.5: Sampling Demonstration
- This demonstration is used to teach students how to distinguish between simple random sampling and stratified sampling and how often random and stratified sampling give exactly the same result.
- 1.4.6: Variables
- Variables are properties or characteristics of some event, object, or person that can take on different values or amounts (as opposed to constants such as π that do not vary). When conducting research, experimenters often manipulate variables. When a variable is manipulated by an experimenter, it is called an independent variable. The experiment seeks to determine the effect of the independent variable on a dependent variable.
- 1.4.7: Percentiles
- A test score in and of itself is usually difficult to interpret. For example, if you learned that your score on a measure of shyness was 35 out of a possible 50, you would have little idea how shy you are compared to other people. More relevant is the percentage of people with lower shyness scores than yours. This percentage is called a percentile.
- 1.4.8: Levels of Measurement
- Before we can conduct a statistical analysis, we need to measure our dependent variable. Exactly how the measurement is carried out depends on the type of variable involved in the analysis. Different types are measured differently. To measure the time taken to respond to a stimulus, you might use a stop watch. Stop watches are of no use, of course, when it comes to measuring someone's attitude towards a political candidate.
- 1.4.9: Measurements
- This is a demonstration of a very complex issue. Experts in the field disagree on how to interpret differences on an ordinal scale, so do not be discouraged if it takes you a while to catch on. In this demonstration you will explore the relationship between interval and ordinal scales. The demonstration is based on two brands of baked goods.
- 1.4.10: Distributions
- Define "distribution" Interpret a frequency distribution Distinguish between a frequency distribution and a probability distribution Construct a grouped frequency distribution for a continuous variable
- 1.4.11: Summation Notation
- Many statistical formulas involve summing numbers. Fortunately there is a convenient notation for expressing summation. This section covers the basics of this summation notation.
- 1.4.12: Linear Transformations
- Often it is necessary to transform data from one measurement scale to another. For example, you might want to convert height measured in feet to height measured in inches.
- 1.4.13: Logarithms
- The log transformation reduces positive skew. This can be valuable both for making the data more interpretable and for helping to meet the assumptions of inferential statistics.
Contributors and Attributions
Online Statistics Education: A Multimedia Course of Study (http://onlinestatbook.com/). Project Leader: David M. Lane, Rice University.