BASICS OF STATISTICS
TYPES OF DATA
SAMPLING & BIAS
EXPERIMENTAL DESIGN
DESCRIPTIVE STATISTICS
100

Define statistics

What is the science of collecting, organizing, analyzing, and interpreting data?

100

Qualitative vs quantitative

Qual = categories, Quan = numbers

100

Simple random sample

Every individual has equal chance

100

Observational vs experiment

Observational = no treatment, Experiment = treatment imposed

100

Mean

Average

200

What is statistical thinking?

Thinking about data in context to make decisions

200

Discrete vs continuous

Discrete = countable, Continuous = measurable

200

Stratified sample

Divide into groups and sample from each

200

Explanatory vs response

Explanatory = causes change, Response = outcome

200

Median

Middle value

300

What is the statistics process?

Ask question → collect data → analyze → interpret

300

Example: qual or quan?

Answers vary (height = continuous, eye color = qualitative)

300

Cluster sample

Randomly select entire groups

300

Placebo  

A fake treatment

300

Mode

Most frequent value

400

Parameter vs statistic

Parameter = population value, Statistic = sample value

400

Which graph for qualitative?

Bar graph

400

Sampling error vs bias

Sampling error is natural variation from using a sample; bias is systematic error

400

Double-blind

Neither subject nor experimenter knows who gets treatment

400

Standard deviation

Measures average distance from the mean

500

Population vs sample

Population = entire group, Sample = subset

500

Which graph for quantitative?

Histogram

500

Types of bias

Non-response bias, response bias, data entry errors

500

Matched pairs vs completely randomized

Matched pairs use related subjects; completely randomized assigns randomly

500

Resistant statistics

Median and IQR

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