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1

Population

Set of units we are interested in studying

2

Variable

Characteristic or property of an individual population unit

3

Census

Act of measuring characteristics on ALL UNITS IN A POPULATION

4

Sample

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

5

Some good statistical practices (good thins to do)

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

6

Descriptive statistics (Def)

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

7

Inference (def) - (Statistical inference)

Generalizing a conclusion from a sample to a larger population

8

Experimental studies (data) generality (2 concepts)

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

9

Key part of experimental study

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

10

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

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

11

Observational studies (difference w/ experimental ?)

We do not control who gets the treatment

12

Confounding variable (def)

Variable that could be causing both the treatment and response variables

13

Problem w/ observational studies

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

14

Verb to use for an observational study's results

We conclude association (not causation)

15

How to eliminate counfounding. What is this called

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

16

Random samples ''synonym''

Mini-populations

17

Selection bias (def)

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

18

Non-response bias

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

19

Measurement error and causes

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

20

Two ways of summarizing data

Numerically and graphically

21

Graphical displays are good for ... (2)

Building and guiding intuition, provide picture of data

22

Numerical summaries are good for ... (2)

Confirming intuition and giving concise impressions

23

Quantitative data are ...

numerical in nature (blood pressure, temperature, ...)

24

Qualitative data are ...

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

25

Derived categories (explanation)

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

26

2 types of charts for qualitative data

Pie charts, Bar charts

27

What could be used tu numerically summarize qualitative data

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

28

What is the Simpson's paradox

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

29

Cause of Simpson's paradox

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

30

Misleading graphs (a couple conclusions)

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