Introduction

Question 1

What are statistics?

Answer 1

Science that relates data to specific questions of interest by devising methods to:

• gather data relevant to question
• summarize and display the data to shed light on the question
• draw conclusions to questions supported by the data

Question 2

Why do we need statistics?

Answer 2

Uncertainty in the data means that we need to determine whether the effects we see are due to chance factors or systematic (nonrandom) factors.

Question 3

What are three primary sources of uncertainty in data collected in social sciences research?

Answer 3

• sampling techniques
• measurement error
• random variability

Question 4

Summarize the main points of Abelson (1995) Chapter 1

Answer 4

• the general public tends to mistrust statistics; however this is not reflective of statistics itself.
• statistical claims have an argumentative nature, some subjectivity is unavoidable.
• making a claim with a single statistic can be deceiving (e.g., average life expectancy of conductors)
• comparisons are important, provide context and reduce likelihood of misinterpretation.
• data analysis & statistical inference help us choose among explanations for observed comparative differences.
• logic of null hypothesis testing (see other card)

Question 5

null hypothesis testing

Answer 5

• testing for systematic differences, two chance factors are at play (sampling, measurement)

3 possible explanations

• variability due to systemic factor (e.g., all control scores are the same)
• variability due to chance factor
• variability due to both chance and systemic factor

First we test all-chance explanation to see if systemic factor should be invoked.

Accept/reject null hypothesis is too strong - use “retain/discredit” instead.

Question 6

Data

Answer 6

numbers that you get from measurements

Question 7

Constant

Answer 7

construct that only has 1 value (e.g., all male sample)

Question 8

Variable

Answer 8

anything that can be codified, has more than 1 value

Question 9

Qualitative/Categorical variable

Answer 9

assigned values don’t mean more or less (e.g., gender)

Question 10

Quantitative/Continuous variable

Answer 10

values indicate an amount (e.g., age)

Question 11

Population

Answer 11

individual or group representing all members of a group of interest

Question 12

Parameter

Answer 12

value generated from or applied to a population

Question 13

Sample

Answer 13

subset of a population

Question 14

Statistic

Answer 14

values derived from sample

Question 15

What are the four scales of measurement and why are they important to know?

Answer 15

They determine what statistical test is needed:

Nominal – different in kind not degree (e.g. gender)
Ordinal - values have weight but don’t tell you distance between scores (e.g., Likert/rating scales)
Interval - values that have weight and equal distances between each unit (e.g., temperature)
Ratio - an interval scale using an absolute zero which means absence of characteristic (e.g. weight)

Question 16

important characteristics of science

Answer 16

empiricism, testability, objectivity

Question 17

descriptive vs inferential statistics

Answer 17

Descriptive statistics uses the data to provide descriptions of the population, either through numerical calculations or graphs or tables. Inferential statistics makes inferences and predictions about a population based on a sample of data taken from the population in question.