Lecture 1

Question 1

Population

Answer 1

Set of units we are interested in studying

Question 2

Variable

Answer 2

Characteristic or property of an individual population unit

Question 3

Census

Answer 3

Act of measuring characteristics on ALL UNITS IN A POPULATION

Question 4

Sample

Answer 4

Subset (Part) of the population on which characteristics are measured

Question 5

Some good statistical practices (good thins to do)

Answer 5

REPRESENTATIVE SAMPLE, ask good questions, make statements we have the data to support

Question 6

Descriptive statistics (Def)

Answer 6

Used to describe observed phenomenons using quantitative tools. Summarize and describe world around us (quantitatively)

Question 7

Inference (def) - (Statistical inference)

Answer 7

Generalizing a conclusion from a sample to a larger population

Question 8

Experimental studies (data) generality (2 concepts)

Answer 8

One attribute of interest (THE TREATMENT) that we have control over and one attribute that we’re interested in observing (THE RESPONSE)

Question 9

Key part of experimental study

Answer 9

We control who gets the treatment (so there is a control group or placebo)

Question 10

Why control who gets treatment in experimental study and why have a control group

Answer 10

To see if it is really the treatment that caused the response

Question 11

Observational studies (difference w/ experimental ?)

Answer 11

We do not control who gets the treatment

Question 12

Confounding variable (def)

Answer 12

Variable that could be causing both the treatment and response variables

Question 13

Problem w/ observational studies

Answer 13

We CAN NOT discount (ignorer/considerer negligeable) confounding variables or factors

Question 14

Verb to use for an observational study’s results

Answer 14

We conclude association (not causation)

Question 15

How to eliminate counfounding. What is this called

Answer 15

Making each subset of the population representative (equal probability of being selected for the sample) -
Random samples

Question 16

Random samples ‘‘synonym’’

Answer 16

Mini-populations

Question 17

Selection bias (def)

Answer 17

A subset of the experimental units in population (a subset of the population/ a subset of populaton that we could measure variable on) has no chance of being selected

Question 18

Non-response bias

Answer 18

Inability to obtain data on all experimental units selected for the sample (some choose ‘‘don’t know’’ or ‘‘no answer’’)

Question 19

Measurement error and causes

Answer 19

Inaccuracies in the values of data recorded (poorly calibrated lab equipement, vague questions, measuring variable that represents large concept)

Question 20

Two ways of summarizing data

Answer 20

Numerically and graphically

Question 21

Graphical displays are good for … (2)

Answer 21

Building and guiding intuition, provide picture of data

Question 22

Numerical summaries are good for … (2)

Answer 22

Confirming intuition and giving concise impressions

Question 23

Quantitative data are …

Answer 23

numerical in nature (blood pressure, temperature, …)

Question 24

Qualitative data are …

Answer 24

Categorical in nature (Hair color, uni major, tumour vs no tumour)

Question 25

Derived categories (explanation)

Answer 25

Qualitative data created quantitative data (ex : pass of fail, high or not high blood pressure, letter grades)

Question 26

2 types of charts for qualitative data

Answer 26

Pie charts, Bar charts

Question 27

What could be used tu numerically summarize qualitative data

Answer 27

Frequencies (how many have this and how many have that)/counts or Percentages/Proportions

Question 28

What is the Simpson's paradox

Answer 28

Situation when a third, counfounding factor, changes the interpretation of the relationship between 2 other QUALITATIVE VARIABLES

Question 29

Cause of Simpson's paradox

Answer 29

Imbalance in the distribution of the categories of the third category w/ respect of the first two (example of the majors

Question 30

Misleading graphs (a couple conclusions)

Answer 30

Use all graph to show variations clearly, Pie chart : no angle of view, always view from top.

Question 31

Frequencies vs proportions

Answer 31

Frequencies can present info in a way more transparent way. Proportions can lead to bad conclusions (like ex. of M/F acceptance at Berkeley)