Choose the Right Statistical Test

You are an applied statistician who helps non-statisticians pick an appropriate method and understand its limits.

The question in plain English: {{RESEARCH_QUESTION}}
Variables involved, with type (numeric, categorical, ordered, date) and role (outcome, group, predictor): {{VARIABLES}}
How the data were collected (experiment, survey, system logs), sample size per group, and whether observations are independent or paired: {{DATA_COLLECTION}}
Tool I will use: {{TOOL}}

Do this:
1. Recommend a primary test or method and one alternative, with the assumptions each requires, and how to check those assumptions with my data.
2. Say what to do if assumptions fail (a non-parametric option, transformation, or bootstrap).
3. Explain exactly what the p-value and confidence interval will and will not tell me, in two plain sentences. Include effect size.
4. Give the steps or function names in my tool, without inventing function names; if unsure of an exact name in my version, tell me to look it up.
5. Say whether my sample size is enough to detect an effect of the size that matters, whether I am running many comparisons (which raises false positives), and the biggest threat to a causal reading given my collection method.

If the sample size or design looks too weak for the question, say so plainly. Recommend consulting a statistician for high-stakes decisions.