Ch1: Intro Stats, Variables

Question 1

READINGS

Question 2

Descriptive stats:

1.1 2 branches of statistics

Answer 2

organize, summarize, and communicate a group of numerical observations; large amounts of data in a single/few #’s

Question 3

Inferential stats

1.1 2 branches of statistics

Answer 3

uses sample data to make estimates about the larger population; infer, or make an intelligent guess about, the population

○ EX: average shark length without measuring every single shark in the world

Question 4

Sample

1.1 2 branches of statistics

Answer 4

• Set of observations drawn from the population of interest
• Most often used because researchers are rarely able to study every person
• Used in inferential

Question 5

Population

1.1 2 branches of statistics

Answer 5

includes all possible observations about which we would like to know something

Question 6

Variables

1.2 How to transform observations into variables

Answer 6

observations of physical, attitudinal, and behavioural characteristics that can take on different values

Question 7

Researchers use both _ and _ numerical observations to quantify variables

1.2 How to transform observations into variables

Answer 7

discreet; continuous

Question 8

Discrete observations

1.2 How to transform observations into variables

Answer 8

can take on only specific values (whole numbers); no other values can exist between these numbers
* EX: 6, not 5.92

Question 9

2 types of observations are always discrete:

1.2 How to transform observations into variables

Answer 9

• Nominal: used for observations that have categories or their names as their values
• Ordinal: used for observations that have rankings (1st, 2nd, 3rd…)

Question 10

Continuous observations:

1.2 How to transform observations into variables

Answer 10

can take on a full range of values an infinite number of potential values exists

Question 11

2 types of observations can be continuous:

1.2 How to transform observations into variables

Answer 11

• Interval: used for observations that have numbers as their values; the distance (or interval) between pairs of consecutive numbers is assumed to be equal
• Some interval variables can be discrete variables - EX: the number of times one has to get up early each week; interval variable because the distance between numerical observations is assumed to be equal (1 and 2 times vs. 5 and 6 times)
• But also discrete, as the number of days in a week cannot be anything but a whole number
• Ratio: variables that meet the criteria for interval variables but also have meaningful zero points

Question 12

Statistics programs often refer to both interval and ratio variables as…

1.2 How to transform observations into variables

Answer 12

• Scale observations
• Scale variable: variable that meets the criteria for an interval variable or a ratio variable

Question 13

Levels

1.3 Variables and Research

Answer 13

the discrete values or conditions that variables can take on

Question 14

3 types of variables:

1.3 Variables and Research

Answer 14

• IV
• DV
• Confounding: any variable that systematically varies with the IV so that we cannot logically determine which variqble is at work

Question 15

Hypothesis testing:

1.4 Introduction to Hypothesis Testing

Answer 15

the process of drawing conclusions about whether a particular relation between variables is supported by the evidence

Question 16

Operational definition:

1.4 Introduction to Hypothesis Testing

Answer 16

specifies the operations or procedures used to measure or manipulate a variable

Question 17

Issue with data being contradictory to one another - how did researchers combat this?

1.4 Introduction to Hypothesis Testing

Answer 17

By developing guidelines for transparent, ethical research (AKA open science) - data ethics: a set of principles related to all stages of working with data - research design, data collection, statistical analyses, interpretation of analyses, and reporting of outcomes.

Question 18

Open science

Data ethics

1.4 Introduction to Hypothesis Testing

Answer 18

an approach to research that encourages collaboration, and includes the sharing of research methodology, data, and statistical analyses in ways that allow others to question and even to try to recreate findings

Question 19

Open science is a “revolution” in response to…

1.4 Introduction to Hypothesis Testing

Answer 19

• Replication failure
• Problems with data collection
• Old-fashioned statistics: Each underscore bad conduct that compounds more mainstream unethical data practices of picking and choosing statistics that make a particular point, misapplying statistical techniques in pursuit of a goal, and creating misleading graphs

Question 20

Solution to the current crisis in psychological science:

1.4 Introduction to Hypothesis Testing

Answer 20

• Severe testing: subjecting a hypothesis to rigorous statistical scrutiny aimed at uncovering any flaws in that hypothesis
• The severe tester = an ethical researcher

Question 21

Preregistration:

1.4 Introduction to Hypothesis Testing

Answer 21

• A recommended open-science practice in which researchers outline their research design and analysis plan before conducting a study (EX: figure skaters giving their routines to judges before actually performing it)
• Details the design of the study

Question 22

What does preregistration do? (3 things)

Answer 22

• Ensures a more severe test of their hypotheses
• Can’t try multiple approaches until one works
• Can’t do HARKing: (H)ypothesizing (A)fter the (R)esults are (K)nown