Flashcards in Lecture 1 Deck (31)
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1
Population
Set of units we are interested in studying
2
Variable
Characteristic or property of an individual population unit
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Census
Act of measuring characteristics on ALL UNITS IN A POPULATION
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Sample
Subset (Part) of the population on which characteristics are measured
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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)
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Inference (def) - (Statistical inference)
Generalizing a conclusion from a sample to a larger population
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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)
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Key part of experimental study
We control who gets the treatment (so there is a control group or placebo)
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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
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Observational studies (difference w/ experimental ?)
We do not control who gets the treatment
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Confounding variable (def)
Variable that could be causing both the treatment and response variables
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Problem w/ observational studies
We CAN NOT discount (ignorer/considerer negligeable) confounding variables or factors
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Verb to use for an observational study's results
We conclude association (not causation)
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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
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Random samples ''synonym''
Mini-populations
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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
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Non-response bias
Inability to obtain data on all experimental units selected for the sample (some choose ''don't know'' or ''no answer'')
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Measurement error and causes
Inaccuracies in the values of data recorded (poorly calibrated lab equipement, vague questions, measuring variable that represents large concept)
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Two ways of summarizing data
Numerically and graphically
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Graphical displays are good for ... (2)
Building and guiding intuition, provide picture of data
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Numerical summaries are good for ... (2)
Confirming intuition and giving concise impressions
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Quantitative data are ...
numerical in nature (blood pressure, temperature, ...)
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Qualitative data are ...
Categorical in nature (Hair color, uni major, tumour vs no tumour)
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Derived categories (explanation)
Qualitative data created quantitative data (ex : pass of fail, high or not high blood pressure, letter grades)
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2 types of charts for qualitative data
Pie charts, Bar charts
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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
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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
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