# Statistical Thinking for Decision Makers Training Course

## Overview

This course has been created for decision makers whose primary goal is not to do the calculation and the analysis, but to understand them and be able to choose what kind of statistical methods are relevant in strategic planning of the organization.

For example, a prospect participant needs to make decision how many samples needs to be collected before they can make the decision whether the product is going to be launched or not.

If you need longer course which covers the very basics of statistical thinking have a look at 5 day “Statistics for Managers” training.

## Requirements

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

## Course Outline

### 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• How decisions make forecast outdated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