Thank you for sharing this workshop. Is there a way to hear about future workshops?

Generally endorsed, but even though I can see the limitations of approaching this in 7.5 minutes I am not sure that the explanation of P is acceptable; as recently summarised [Ioannidis, Eur Heart J. 40(31)]; 1. P-values can indicate how incompatible the data are with a specified statistical model. 2. P-values do not measure the probability that the studied hypothesis is true, or the probability that the data were produced by random chance alone. 3. Scientific conclusions and business or policy decisions should not be based only on whether a P-value passes a specific threshold. 4. Proper inference requires full reporting and transparency 5. A P-value, or statistical significance, does not measure the size of an effect or the importance of a result. 6. By itself, a P-value does not provide a good measure of evidence regarding a model or hypothesis.

Great job!