2.17 Unit 2 summary

Use Monte Carlo simulation to explore patterns in a random process:

Model: Translate real life phenomena into a model to be used in the simulation process.
Simulate: Use TinkerPlots™ to generate random outcomes from a model.
Evaluate: Determine the “typical” result from a Monte Carlo model, and a range of likely results

Conduct a statistical hypothesis test of an observed result, including:

Write an appropriate null hypothesis that specifies a “no effect” probability model and a source of variation
Use Monte Carlo simulation in TinkerPlots™ to simulate a study if the null hypothesis were true
- Model: Use a sampler to model the study if the null hypothesis were true
- Simulate: Run the simulation hundreds of times and collect the result of interest.
- Evaluate:
  - Find a range of likely results if the null hypothesis were true
  - Determine whether the observed result is compatible with the null hypothesis
Calculate a p-value
Formulate a conclusion

The logic behind statistical hypothesis testing, including:

Regularity in randomness
The role of the null hypothesis as specifying a baseline to compare the observed result to
Why we use Monte Carlo simulation
Why we need to run multiple trials and when we have run enough
What the distribution of results represents
What we are checking for, in order to determine whether the observed result is compatible with the null model
What a p-value represents
The sort of conclusions we can (and can’t) make from a statistical hypothesis test.

Create a new sampler and use different devices to model a null hypothesis
Plot values from a table and organize (by separating) the the plotted values.
Numerically summarize values in a plot (e.g., Count (N), Count (%)).
Automatically collect the results from many trials.
Use the Reference line and Divider tools to count the values in a distribution that are as or more extreme than a given value