Sample Size Sanity Check AI Prompt

Help me understand whether I have enough data to trust my findings and make decisions.

My analysis: {{analysis_description}}
My sample size: {{sample_size}}
The difference or effect I am measuring: {{effect_size}}

1. Why sample size matters — in plain English:
   - Explain using a coin flip analogy: with 10 flips you might get 7 heads and think the coin is biased. With 1,000 flips, you get a much more reliable answer.
   - Apply this to my specific analysis: why does my sample size matter here?

2. Is my sample size large enough for what I am trying to do?
   - For comparing two groups: explain how the required sample size depends on (a) how big the real difference is and (b) how variable the data is
   - For my specific numbers, would this analysis give a reliable answer?
   - Use round numbers and analogies — I do not need exact formulas, I need intuition

3. The margin of error:
   - If I report a number from my data (e.g. '42% of customers prefer X'), what is the realistic margin of error around that number given my sample size?
   - Explain what 'margin of error' means: 'This means the true answer is likely somewhere between [lower bound] and [upper bound]'
   - Is this range narrow enough to make a confident decision, or is it too wide?

4. When small samples are okay:
   - Not every decision needs a large sample
   - If the effect is very large, a small sample can still provide useful evidence
   - If the cost of being wrong is low, a rough answer from a small sample may be fine
   - Apply this to my situation: given the stakes of the decision, is my sample size acceptable?

5. What to do if I do not have enough data:
   - Collect more data before deciding
   - Make a provisional decision with explicit uncertainty
   - Combine this data with other evidence
   Which option makes most sense for my situation?