Statistics for Decision Makers - 01.00 - Outline
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Statistics…。
- "There are three kinds of lies
- lies, damned lies, and statistics."
- Is it just yet another statistic?
- Or is it just a lie?
- It is just a lie!
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 the P-value
- Repeated experiment - how to interpret repeated experiment results
- Data collection - you can minimize bias, but not get rid of it
- Understanding confidence levels
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, decision trees)
- How errors add up
- The Butterfly Effect
- The Cassandra Problem - how to measure a forecast if the course of action has changed
- How decisions make forecasting outdated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 forecasts
Statistical Methods that are 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
- The logic of Hypothesis Testing
- What can be proven, and why it is always the opposite of what we want (Falsification)
- Interpreting the results of Hypothesis Testing
- Testing Means
- Power
- How to determine a good (and cheap) sample size
- False positives and false negatives and why it is always a trade-off
Statistics。
- Descriptive (without extrapolating)
- Used for:
- Comparing populations
- Tracking trends (e.g. profit)
- Inferential (uses probability to extrapolate)
- Using samples to infer what a population looks like
- Using past data to infer the future (forecasting)
Probability。
- Frequentist approach
- Probabilities can be combined (addition, multiplication)
- Usually probabilities are conditional (probability of A assuming B)
Statistical Errors。
- Error of Measurement
- What is the margin of error of the device we use to measure?
- Bias of Sampling Method
- Is the sample really representative?
- False Detection (false positive, Type I)
- What is the probability of finding a difference where in reality there is no difference?
- Missed Detection (false negative, Type II)
- What is the probability of not finding the difference where there is one?
- Forecast Error
- How much the forecast differs from the real thing
Control Charts。
- Early warning system that something has changed
- Is the average page response time slowing down or were the last samples just unusually slow?
- Is the response rate from customers exceptionally low?
- Should we stop and investigate the increased response time or ignore it?
Auto-regression。
- Are changes in the past related to changes in the future?
- Are sales in March always lower on average than in other months of the year?
