I am trying to figure out what would be the best way to analyze data from a randomized double blind trial we conducted. We sought to find if two dosages of a drug were effective for a severe symptom in patients with an advanced stage of a disease in which this symptom is very common (affecting approximately 40% of patients). We used a 100 mm visual analog scale to assess the symptom and decided to include only patients with severe symptom intensity, defined as more than 50 mm on the visual analog scale. A sample size calculation for parametric tests was conducted, using mean VAS and standard deviation from previous studies on the subject. These indicated that the drug had dramatic effects, so a large effect was used in the sample size calculation. The calculated sample size was n=8 per group. Here are my questions:
1. I do not know if I can make a normality assumption. How would I know?
2. If I can make a normality assumption but the sample size calculation gives me small numbers, should I use parametric tests even though the numbers are so small I cannot assume they have a normal distribution?
3. Do normality tests (KS, Shapiro Wilk) make sense on such small numbers? I mean: if such tests do indeed tell me my sample has a normal distribution, are they reliable if the numbers are so small?
4. Is it legit to use non-parametric tests even though I originally calculated the sample using a formula intended for parametric tests?