Introduction Chapter 1 of Social Statistics

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1 Introduction p.1/22 Introduction Chapter 1 of Social Statistics Chris Lawrence

2 Introduction p.2/22 Introduction In this chapter, we will discuss: What statistics are

3 Introduction p.2/22 Introduction In this chapter, we will discuss: What statistics are The difference between a statistic and a parameter

4 Introduction p.2/22 Introduction In this chapter, we will discuss: What statistics are The difference between a statistic and a parameter Samples and populations

5 Introduction p.2/22 Introduction In this chapter, we will discuss: What statistics are The difference between a statistic and a parameter Samples and populations Descriptive and inferential statistics

6 Introduction p.3/22 Why care about statistics? Understanding statistics is important. If you don t understand statistics, then whenever you come across statistically-based information, you ll have just two choices: Unconditionally accept the information.

7 Introduction p.3/22 Why care about statistics? Understanding statistics is important. If you don t understand statistics, then whenever you come across statistically-based information, you ll have just two choices: Unconditionally accept the information. Find someone else to interpret it for you.

8 Introduction p.4/22 We are surrounded by statistics We encounter statistics all of the time in everyday life, even though their role is not always obvious! Car and health insurance rates are based on stats.

9 Introduction p.4/22 We are surrounded by statistics We encounter statistics all of the time in everyday life, even though their role is not always obvious! Car and health insurance rates are based on stats. Politicians make decisions based on public opinion polls.

10 Introduction p.4/22 We are surrounded by statistics We encounter statistics all of the time in everyday life, even though their role is not always obvious! Car and health insurance rates are based on stats. Politicians make decisions based on public opinion polls. We rely on stats for crime rates, consumer information, and weather reports.

11 Introduction p.5/22 What is political science? For a moment, let us revisit Shively... The goal of social science is to describe and explain different types of social phenomena.

12 Introduction p.5/22 What is political science? For a moment, let us revisit Shively... The goal of social science is to describe and explain different types of social phenomena. In political science, we want to describe and explain political phenomena.

13 Introduction p.6/22 Characteristics and variables So, we want to understand how social or political characteristics relate to and affect one another.

14 Introduction p.6/22 Characteristics and variables So, we want to understand how social or political characteristics relate to and affect one another. These characteristics are also called variables.

15 Introduction p.7/22 All variables must vary Variables vary. Things that don t vary can t be variables.

16 Introduction p.7/22 All variables must vary Variables vary. Things that don t vary can t be variables. Variables change in some measurable way from unit to unit.

17 Introduction p.7/22 All variables must vary Variables vary. Things that don t vary can t be variables. Variables change in some measurable way from unit to unit. For example, a person s weight, income, and party identification are examples of variables.

18 Introduction p.8/22 Dependent and independent variables When we examine relationships between variables, we normally examine the change in one variable that is caused by another.

19 Introduction p.8/22 Dependent and independent variables When we examine relationships between variables, we normally examine the change in one variable that is caused by another. The variable that is being changed is the dependent variable.

20 Introduction p.8/22 Dependent and independent variables When we examine relationships between variables, we normally examine the change in one variable that is caused by another. The variable that is being changed is the dependent variable. The variable or variables that cause change are the independent variable or variables.

21 Introduction p.9/22 Characteristics, attitudes, and behavio There are three major types of variable we can measure: Characteristics: gender (male/female), religion.

22 Introduction p.9/22 Characteristics, attitudes, and behavio There are three major types of variable we can measure: Characteristics: gender (male/female), religion. Attitudes: like/dislike math.

23 Introduction p.9/22 Characteristics, attitudes, and behavio There are three major types of variable we can measure: Characteristics: gender (male/female), religion. Attitudes: like/dislike math. Behavior: embrace/avoid math.

24 Introduction p.10/22 Examples American politics: Republican, hates Al Gore, voted for George W. Bush.

25 Introduction p.10/22 Examples American politics: Republican, hates Al Gore, voted for George W. Bush. German politics: Social Democrat, likes European Union, voted for SPD.

26 Introduction p.10/22 Examples American politics: Republican, hates Al Gore, voted for George W. Bush. German politics: Social Democrat, likes European Union, voted for SPD. International relations: Country X is a theocracy, intolerant of other religions, goes to war with infidels

27 Populations versus samples A population is everyone or everything that is in our universe of interest. Examples: everyone in the United States, all of the countries in the world, every fraternity member at Ole Miss, every pop singer between 15 and 30. Introduction p.11/22

28 Populations versus samples A population is everyone or everything that is in our universe of interest. Examples: everyone in the United States, all of the countries in the world, every fraternity member at Ole Miss, every pop singer between 15 and 30. The population we examine is usually determined by our research question. Introduction p.11/22

29 Populations versus samples A population is everyone or everything that is in our universe of interest. Examples: everyone in the United States, all of the countries in the world, every fraternity member at Ole Miss, every pop singer between 15 and 30. The population we examine is usually determined by our research question. For example, if we wanted to know why people vote the way they do, our population would be voters. Introduction p.11/22

30 Introduction p.12/22 Populations can be big So if we care about vote choice, we would have a very large population: Approximately 270 million citizens in the U.S.

31 Introduction p.12/22 Populations can be big So if we care about vote choice, we would have a very large population: Approximately 270 million citizens in the U.S. Approximately 180 million are over 18 (2/3)

32 Introduction p.12/22 Populations can be big So if we care about vote choice, we would have a very large population: Approximately 270 million citizens in the U.S. Approximately 180 million are over 18 (2/3) Approximately 90 million of those vote (1/2)

33 Parameters: math about populations When we have some math that tells us something about an entire population, we call that math a parameter. The average income of all Americans. Introduction p.13/22

34 Parameters: math about populations When we have some math that tells us something about an entire population, we call that math a parameter. The average income of all Americans. The percentage of all voters who are Democrats. Introduction p.13/22

35 Parameters: math about populations When we have some math that tells us something about an entire population, we call that math a parameter. The average income of all Americans. The percentage of all voters who are Democrats. The average GPA of all fraternity members at Ole Miss. Introduction p.13/22

36 Parameters: math about populations When we have some math that tells us something about an entire population, we call that math a parameter. The average income of all Americans. The percentage of all voters who are Democrats. The average GPA of all fraternity members at Ole Miss. The number of countries in the world that have a theocratic government. Introduction p.13/22

37 Parameters: math about populations When we have some math that tells us something about an entire population, we call that math a parameter. The average income of all Americans. The percentage of all voters who are Democrats. The average GPA of all fraternity members at Ole Miss. The number of countries in the world that have a theocratic government. The percentage of pop stars from 15 to 30 who have had cosmetic surgery. Introduction p.13/22

38 Introduction p.14/22 Parameters: about the population Parameters are always descriptive: they always describe everyone in the population.

39 Introduction p.14/22 Parameters: about the population Parameters are always descriptive: they always describe everyone in the population. When we talk about parameters, we use lower case greek letters, like σ (sigma). The size of the population is N.

40 Introduction p.14/22 Parameters: about the population Parameters are always descriptive: they always describe everyone in the population. When we talk about parameters, we use lower case greek letters, like σ (sigma). The size of the population is N. Parameters are usually collected through a procedure like a census or by keeping track of every event that happens.

41 Introduction p.15/22 Samples When we don t have the entire population, we have a sample.

42 Introduction p.15/22 Samples When we don t have the entire population, we have a sample. For example, we can t collect information on all voters (why not?), so we have to take a sample.

43 Introduction p.15/22 Samples When we don t have the entire population, we have a sample. For example, we can t collect information on all voters (why not?), so we have to take a sample. With an appropriate sample, we can estimate the parameters of the population.

44 Introduction p.16/22 Sampling from a population To estimate the parameters, we need a representative sample that is randomly selected.

45 Introduction p.16/22 Sampling from a population To estimate the parameters, we need a representative sample that is randomly selected. For example, we can estimate the percentage of voters who are Democrats by getting data from a sample of voters.

46 Introduction p.17/22 Statistics: math based on samples Statistics are the mathematics associated with samples.

47 Introduction p.17/22 Statistics: math based on samples Statistics are the mathematics associated with samples. We usually use upper-case Latin letters for statistics (like S). The size of a sample is denoted by n.

48 Introduction p.17/22 Statistics: math based on samples Statistics are the mathematics associated with samples. We usually use upper-case Latin letters for statistics (like S). The size of a sample is denoted by n. Sample statistics are estimates of population parameters.

49 Not all statistics are statistics In the real world, a lot of things we call statistics are actually parameters. Barry Bonds batting average is based on every at-bat during the season, so it is actually a parameter. Introduction p.18/22

50 Not all statistics are statistics In the real world, a lot of things we call statistics are actually parameters. Barry Bonds batting average is based on every at-bat during the season, so it is actually a parameter. Eli Manning s pass completion percentage is also a parameter, because it is based on all of his passes in games. Introduction p.18/22

51 Not all statistics are statistics In the real world, a lot of things we call statistics are actually parameters. Barry Bonds batting average is based on every at-bat during the season, so it is actually a parameter. Eli Manning s pass completion percentage is also a parameter, because it is based on all of his passes in games. The number of deaths on a highway in a year is a parameter, not a statistic. Introduction p.18/22

52 Introduction p.19/22 Making good estimates Again, for our sample statistics to be good estimates of population parameters, our sample must be: representative: the makeup of the sample should reflect the makeup of the population of interest.

53 Introduction p.19/22 Making good estimates Again, for our sample statistics to be good estimates of population parameters, our sample must be: representative: the makeup of the sample should reflect the makeup of the population of interest. random: the chance of any two members of the population being in the sample must be equal.

54 Bad estimation strategies Our statistics won t be any good if we have a bad sample. If we care about all voters in the United States, we can t use a sample of voters from Memphis. Introduction p.20/22

55 Bad estimation strategies Our statistics won t be any good if we have a bad sample. If we care about all voters in the United States, we can t use a sample of voters from Memphis. If we want to find out about all Ole Miss students, we can t take a sample from people in the student section at an Ole Miss basketball game. Introduction p.20/22

56 Bad estimation strategies Our statistics won t be any good if we have a bad sample. If we care about all voters in the United States, we can t use a sample of voters from Memphis. If we want to find out about all Ole Miss students, we can t take a sample from people in the student section at an Ole Miss basketball game. If we want to find out about pop singers from 15 to 30, we can t just talk to the ones who have dated Carson Daly. Introduction p.20/22

57 Introduction p.21/22 Uses of statistics We use statistics for two major reasons: Estimating population parameters. (For example, estimating the percentage of all voters who are Democrats.)

58 Introduction p.21/22 Uses of statistics We use statistics for two major reasons: Estimating population parameters. (For example, estimating the percentage of all voters who are Democrats.) Testing hypotheses about populations. (For example, finding out whether Democratic voters were more likely to vote for Al Gore than Republicans.)

59 Introduction p.22/22 Two types of statistics Descriptive statistics are like population parameters; they simply describe the sample.

60 Introduction p.22/22 Two types of statistics Descriptive statistics are like population parameters; they simply describe the sample. Inferential statistics allow us to make generalizations (or inferences) about populations. Both uses of statistics in the previous slide are inferential uses of statistics.

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