Lecture 1: Introduction to Statistics (Chapter 1)

Question 1

Why are Statistics needed?

Answer 1

Stats are the best set of tools to decide if a statement is true

Question 2

What is an inductive statement?

Answer 2

A statement whose truth can be assessed by collecting and analyzing data

Question 3

Types of Statistical Analysis

Answer 3

1. Descriptive Statistics
2. Inferential Statistics

Question 4

Descriptive Statistics?

Answer 4

• Numbers that are used to summarize and describe data
• Good at telling us what our data looks like
• NOT ABLE TO GENERALIZE

Question 5

Data?

Answer 5

Information collected from a survey etc.

Question 6

Inferential Statistics?

Answer 6

• Helps us generalize our sample back up to our population, easy to generalize the information
  2 TYPES
  1. T-stats
  2. F-Stats

Question 7

T-stats?

Answer 7

used to determine if there is a change in 2 groups over time

Question 8

F-stats?

Answer 8

used to determine if there is a change in multiple groups over time

Question 9

Difference between Population and a Sample

Answer 9

Population:
- Members of the groups
e.g. All of York University
Sample:
- Subset of a population
e.g. Psychology students at York University

Question 10

Sampling Bias:

Answer 10

Conclusions made that aren’t generalizable
e.g. taking only male psychology students in york may cause a more biased result

Question 11

Sampling Error:

Answer 11

Discrepancy of how accurate inference is
i.e. how “off” the information is

Question 12

Random Sampling:

Answer 12

Every member of the population has an equal chance of being selected

Question 13

Sample Size:

Answer 13

How big the sample is
LARGER SAMPLE = MORE REPRESENTATIVE

Question 14

Why would you use a more complex sampling?

Answer 14

Because you aren’t able to build the sample randomly

Question 15

Stratified sampling:

Answer 15

• Creating subsets and selecting randomly from them
• NOT MATHEMATICALLY RANDOM
  e.g. from all york students you randomly selected males

Question 16

Convenience sampling:

Answer 16

Finding the easiest/ most accessible participants, usually a follow-up survey
e.g. URPP

Question 17

Different scales of measurement

Answer 17

1. Nominal/Categorical Variables
2. Ordinal Scale
3. Interval Scale
4. Ratio Scale

Question 18

Differences between all scales of measurement

Answer 18

1. Nominal
   - can be scaled
2. Ordinal
   - AND ranked
3. Interval
   - AND evenly spaced
4. Ratio
   - AND has a natural scale

Question 19

Continuous vs Discrete Variables

Answer 19

Continuous Variables are continuous
Discrete variables are not continuous

Question 20

Independent variable (IV)

Answer 20

The variable that explains outcomes
e.g. “x” in y=mx+b

Question 21

Dependant variable (DV)

Answer 21

The variable that is being explained
e.g. “y” in y=mx+b

Question 22

Confounding variable

Answer 22

Variables that you can control and randomize away
e.g. factors other than “x” that can affect “y”

Question 23

Reliability:

Answer 23

Tells us the measurements and how consistent they are
e.g. a weight scale

Question 24

Validity:

Answer 24

Tells us the accuracy of the measurement
e.g. if we have a bag of potatoes when we step on a scale, its not valid cause its not our real weight

Question 25

Types of Research Design

Answer 25

1. Non-Experimental Designs
2. Experimental Designs

Question 26

Non-Experimental Designs

Answer 26

Correlational research
- Measuring the relationship between 2 variables

Question 27

Experimental Design

Answer 27

When we want to know if there is a cause for a change
- usually observations

Question 28

Replication:

Answer 28

Experiments that use the same procedure as a previous one but with a new sample from the same population

Question 29

Type 1 error:

Answer 29

When you think it is right but it is not
- False Positive

Question 30

Type 2 error:

Answer 30

When you think it is wrong but it is right
- False Negative

Question 31

Variables:

Answer 31

Factors that change

Question 32

Constant:

Answer 32

Factors that don’t change

Question 33

Methods of Data Collection:

Answer 33

1. Independent Design
2. Repeated-measures Design

Question 34

Independent Design:

Answer 34

Manipulates the IV using different participants where different groups take part

Question 35

Repeated-measures design:

Answer 35

Manipulate the IV using the same participants

Question 36

Data Ethics:

Answer 36

Principles relating to all stages of working with data

Question 37

Open Science:

Answer 37

Research that encourages collaboration, sharing of methodologies, data

Question 38

Data-Related Problems:

Answer 38

Replication Failures:
- Researchers are unable to reproduce/ replicate findings
Problems with Data Collection:
- Researchers design studies and collect data that helps them get what they want
(may not be the most accurate but gives you the results that you want)
Old-Fashioned Statistics:
- Traditional ways of analyzing data that can lead to inaccurate outcomes

Question 39

HARKing:

Answer 39

Hypothesizing After the Results are Known

Question 40

Preregistration:

Answer 40

Recommended open-science practice where researchers outline their designs and analysis before conducting the study