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Statistical Thinking

What is statistics?

Example
  • Descriptive Statistics: Calculating the average income in a city
  • Inferential Statistics: Predicting election outcomes based on sample data

Types of Data

Example
  • Discrete: Number of books read in a year
  • Continuous: Measuring temperature over time
  • Nominal: Types of fruits - apple, banana, cherry
  • Ordinal: Customer satisfaction rating - satisfied, neutral, dissatisfied

Sampling Methods

Example
  • Simple Random Sample: Drawing random student IDs from a list
  • Systematic Sample: Selecting every 10th person on a roster
  • Stratified Sample: Dividing by age groups, then sampling from each
  • Cluster Sample: Selecting a few schools, then surveying all students in those schools
  • Convenience Sample: Asking passersby at a mall for opinions
Sampling error
  • Despite random sampling method, there may still be discrepancy between sample result and the actual population result
  • This occurs because of chance fluctuations in the sample selection
  • Combat this error by taking larger samples

Experimental Design and Ethics

Questions to ask about the source of the study:
  • Who is reporting this data?
  • Are there conflicts of interest?
  • Who is funding the study?
Questions to ask about the statistics:
  • Was there a large enough unbiased sample?
  • Is the data new and relevant or old and outdated?
  • Are complete details of the methods and assumptions provided?
  • Was the study peer-reviewed by experts before publication?