# Lesson 1: Intro to Statistical Research Methods

• To have some level of confidence in survey results, you would need to know, at least:
• how many people were surveryed
• who was surveyed
• how the survey was conducted
• Constructs
• Things that are generally difficult to measure precisely eg:
• Happiness
• Hunger
• Health
• Operational Definition
• Systems to measure constructs
• Samples & population

• Average across population is called "population parameter". Denoted by 'mu'
• Within population there's a "sample" of people.
• Average for the sample is called "sample statistics". Denoted by 'x-bar'
• Difference between mu and x-bar is called "sampling error"

• Obviously, the bigger the sample, the closer the mu is to x-bar population
• Visualize relationship
• Helps to find correlations between data
• Correlation does not imply causation
• "No two countries with a McDonald's have ever gone to war since opening the McDonald's" -- Thomas Friedman
• Casual inference
• To observe pattern between two variables, have to consider "lurking variables"
• To show relationships, you can do observational studies or surveys
• To show causation, you'd need to do a "controlled experiment"
• Downsides to surveys
• Untruthful responses
• Biased responses
• People not understanding the questions or refusing to answer
• Controlled Experiments
• Placebo
• Allows for a comparison 'control group' to the 'experimental group'
• Blind
• Ensures everyone thinks they're the 'experimental group'
• Double blind
• Researchers observing results shouldn't know who's in control or experimental to prevent judgement bias
• Independant variable = variable that is changed to test effect on dependant variables
• Dependant variable = variable being tested