# Statistical Thinking for Decision Makers培训

statdm

## 课程时长

7 小时 通常来说是1天，包括中间休息。

## 要求

Good maths skills are required. Exposure to basic statistics (i.e. working with people who do the statistical analysis) is required.

## 课程概览

Machine Translated

## 课程大纲

### What statistics can offer to Decision Makers

• Descriptive Statistics
• Basic statistics - which of the statistics (e.g. median, average, percentiles etc...) are more relevant to different distributions
• Graphs - significance of getting it right (e.g. how the way the graph is created reflects the decision)
• Variable types - what variables are easier to deal with
• Ceteris paribus, things are always in motion
• Third variable problem - how to find the real influencer
• Inferential Statistics
• Probability value - what is the meaning of P-value
• Repeated experiment - how to interpret repeated experiment results
• Data collection - you can minimize bias, but not get rid of it
• Understanding confidence level

### Statistical Thinking

• Decision making with limited information
• how to check how much information is enough
• prioritizing goals based on probability and potential return (benefit/cost ratio ration, decision trees)
• Butterfly effect
• Black swans
• What is Schrödinger's cat and what is Newton's Apple in business
• Cassandra Problem - how to measure a forecast if the course of action has changed
• Google Flu trends - how it went wrong
• How decisions make forecast outdated
• Forecasting - methods and practicality
• ARIMA
• Why naive forecasts are usually more responsive
• How far a forecast should look into the past?
• Why more data can mean worse forecast?

### Statistical Methods useful for Decision Makers

• Describing Bivariate Data
• Univariate data and bivariate data
• Probability
• why things differ each time we measure them?
• Normal Distributions and normally distributed errors
• Estimation
• Independent sources of information and degrees of freedom
• Logic of Hypothesis Testing
• What can be proven, and why it is always the opposite what we want (Falsification)
• Interpreting the results of Hypothesis Testing
• Testing Means
• Power
• How to determine a good (and cheap) sample size
• False positive and false negative and why it is always a trade-off

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