In many cases, a paired sample or two sample t-test can be used to make these arguments. However, if the underlying distribution is fundamentally different from a Gaussian, then approaches such as bootstrap sampling are appropriate. More on this soon.
Present one data figure that illustrates something interesting about the structure of the data.
You will present your own slides over Zoom.
Structuring a quality poster is a challenge. In general, your aim should be for the poster to somewhat "stand-alone" (a visitor can understand the essence of your poster without you presenting it). However, your poster should not be packed with a tremendous amount of detail, or even full sentences. It is not uncommon for some some sections to be bulleted lists of talking points expressed using key phrases.
Your poster should include these sections:
Describe comparative results across multiple models and/or hyper-parameter sets. Which choice(s) are best? Make a clear statistical argument here using test set results. One appropriate approach is to compare test set performance for N folds for multiple models. A two-sample or paired t-test can often be used here. However, if the performance measures do not fall within a normal distribution, then it is appropriate to use a sampling-based approach (e.g., a bootstrap test of means).
Last modified: Tue Mar 24 14:49:32 2020