What is statistics? Only one of the most awesome types of math EVER! Statistics helps us make sense of the world around us by providing methods to describe and analyze data. Data comes in many forms:

Type of dataDescriptionExamples
DiscreteData for which there are only whole number valuesNumber of people, Number of countries you’ve visited, price in cents
ContinuousData for which any value in-between whole numbers is possibleHeight, distance, length of time
NominalData listing names of somethingType of relative (mom, aunt, uncle, cousin, grandfather), type of car
OrdinalData that describes the rank/orderYear in school (1st grade, 2nd grade), choice on rating scale (e.g., 1-5), months
IntervalNumerical, ordinal data with evenly-spaced intervalsDegrees in Fahrenheit
RatioInterval data where 0 has a clear meaningTime

This book focuses on describing and analyzing continuous data, but in Lesson 16 we get into categorical data.

The examples of each type of data above are variables. The actual data values collected for each variable vary; for example, if we recorded the temperature every evening at 8:00 pm (where the variable is “degrees in Fahrenheit”), we would record many different values.

Always analyze data with a critical eye. You should know how a survey or experiment was conducted, who is in the sample, and how the variables are measured. This textbook will help you develop a sense for numbers so that you can tell if something fishy is going on.

For one thing, it’s crucial that you always know exactly how variables are measured. If the variable is height, are you measuring in inches? Centimeters? It’s relatively easy to determine a unit of measurement for height, but what about the variable happiness? Happiness, love, ambition, etc. are examples of constructs, variables that can’t be easily defined or measured.

For constructs, we have to determine an operational definition; in other words, a precise way of measuring them. For example, maybe we could measure happiness by the number of times people smile or laugh each day.

For constructs, we have to determine an operational definition; in other words, a precise way of measuring them. For example, maybe we could measure happiness by the number of times people smile or laugh each day.

Throughout this book, you’ll use operational definitions to analyze variables and the relationships between them. You’ll also learn how to draw conclusions about an entire population (e.g., all residents of the United States) based on actual population data or a sample of that population (e.g., 5000 randomly selected residents of the United States). Samples must be representative of a population in order to draw inferences about the population; for this to happen, the sample should be chosen randomly, meaning that each member of the population has an equal chance of being chosen to be in the sample.


This is a preview of Lesson 1. To access the full book, please purchase a hard copy or a digital version. If you opt for the digital version, you will receive a link via email within 1 business day.

Continue to Lesson 2, or select a lesson below.

Street-Smart Stats homepage
Lesson 1: Introduction to Statistical Research Methods
Lesson 2: Visualizing Data
Lesson 3: Central Tendency
Lesson 4: Variability
Lesson 5: Standardizing
Lesson 6: Normal Distribution
Lesson 7: Sampling Distributions
Lesson 8: Estimation
Lesson 9: Hypothesis Testing
Lesson 10: t-Tests for Dependent Samples
Lesson 11: t-Tests for Independent Samples
Lesson 12: Intro to One-Way ANOVA
Lesson 13: One-Way ANOVA: Test significance of differences
Lesson 14: Correlation
Lesson 15: Linear Regression
Lesson 16: Chi-Squared Tests
Afterward
Index

2 thoughts on “Lesson 1: Introduction to Statistical Research Methods

    1. Hi Rick! Just download the Google spreadsheet as a csv and then save it to your working directory. When you input it into R (method 2 above), just make sure you use the same file name. Lesson 2 page 1 has an R tutorial that uses data from a Google spreadsheet and walks you through inputting it into R.

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