General structure of a simulation

  1. Define what type of data and variables need to be simulated, i.e. their distribution, their class (e.g. factor vs. numerical values), sample sizes (within a dataset and number of repetitions), what will need to vary (e.g. the strength of relationship), etc.

  2. Generate data, random data or data including an effect (e.g. an imposed correlation between two variables).

  3. Run the statistical test you think is appropriate and record the relevant statistic (e.g. p-value).

  4. Repeat step 2 and 3 to get the distribution of the statistic of interest.

  5. Try out different parameter sets (explore the parameter space for which results are similar).

  6. Analyse and interpret the combined results of many simulation repetitions within each set of parameters. For instance, check that you only get a significant result in 5% of the repetitions (if alpha = 0.05) when you simulated no effect and that you get a significant result in 80% of the repetitions (if you targeted a power of 80%) when you simulated an effect.

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