Course Outline

What Statistics Can Offer to Decision Makers for Government

  • Descriptive Statistics
    • Basic statistics - identifying which statistics (e.g., median, average, percentiles) are most relevant to different distributions
    • Graphs - the significance of accurate graph creation and how it reflects decision-making
    • Variable types - understanding which variables are easier to manage
    • Ceteris paribus, things are always in motion
    • Third variable problem - techniques for identifying the true influencer
  • Inferential Statistics
    • Probability value - the meaning and interpretation of P-value
    • Repeated experiment - interpreting results from repeated experiments
    • Data collection - methods to minimize bias, though not eliminate it entirely
    • Understanding confidence levels in statistical analysis

Statistical Thinking for Government Decision Makers

  • Decision making with limited information
    • Assessing how much information is sufficient
    • Prioritizing goals based on probability and potential return (benefit/cost ratio, decision trees)
  • How errors add up
    • The Butterfly Effect in statistical contexts
    • Black swans and their impact on forecasts
    • Understanding Schrödinger's cat and Newton's Apple in a business context
  • Cassandra Problem - measuring forecast accuracy when actions have changed
    • Case study: Google Flu trends and its shortcomings
    • How decisions can render forecasts outdated
  • Forecasting - methods and practicality
    • ARIMA (AutoRegressive Integrated Moving Average) models
    • Why naive forecasts are often more responsive
    • Determining the appropriate historical data range for forecasting
    • Why more data can sometimes lead to worse forecasts

Statistical Methods Useful for Government Decision Makers

  • Describing Bivariate Data
    • Differentiating between univariate and bivariate data
  • Probability
    • Understanding why measurements vary each time they are taken
  • Normal Distributions and normally distributed errors
  • Estimation
    • The role of independent sources of information and degrees of freedom
  • Logic of Hypothesis Testing
    • What can be proven, and the concept of falsification
    • Interpreting the results of hypothesis testing
    • Testing means in statistical analysis
  • Power
    • Determining an effective and cost-efficient sample size
    • The trade-offs between false positive and false negative results

Requirements

Strong mathematical skills are required. Experience with basic statistics, or exposure to working alongside professionals who conduct statistical analysis, is also necessary for government.

 7 Hours

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