Designs and what they support
Eleven designs are available. What matters is which ones support a population inference: probability designs do, non-probability designs do not, and the platform enforces that rather than trusting the label on the study.
Representativeness is not a setting
A non-probability design cannot claim representativeness, regardless of what the study declares. If you set the flag, it is forced back to false. Such a design also cannot be post-stratified — producing weights would imply a sampling frame that never existed.
Sample size
Sample-size calculation runs Cochran, then the finite-population correction, then the design effect, then non-response — in that order — and returns every step, so the number can be checked rather than trusted.
Weights
Design weights, post-stratification and normalisation are available. Zero and negative weights are errors, not warnings. Every result states whether it is UNWEIGHTED, WEIGHTED or NOT_APPLICABLE.
Weighted variance uses the Kish effective sample size approximation. This is not design-based variance, and every weighted interval carries that as a stated assumption — it will understate uncertainty for a clustered design.