Newbie, so please bear with me.. I'm trying to get my head around how I can use the awesome tool I found and what it does not give me any info about.
The case: Using this site: http://hedwig.mgh.harvard.edu/sample_size/js/js_parallel_quant.html I have made a pilot study with 7 measurements and stated that I want a power of 80%.
It gives me this output:
The provided parameters were: significance level (adjusted for sidedness) = 0.025, standard deviation = undefined, number of patients = 7, power = 0.8, difference in means = undefined, location of mean in one group as a percentile of the other group = undefined.
The variable calculated was the minimum detectable difference.
A total of 7 patients will enter this two-treatment parallel-design study. The probability is 80 percent that the study will detect a treatment difference at a two-sided 0.05 significance level, if the true difference between treatments is 2.661 times the standard deviation.
is 2.661 the z-score; _________?
I consider 2.661 as a min. resolution that I'm able to differentiate between two different means. Is that a wrong assumption; ___________?
Would plotting the mean with top and bottom error bars with length of 2.661 x STD be the 95% confidence interval; ___________?
A concrete example were I wish to use this knowledge: I wish to know something about the packing of spheres in a cylinder by dividing the total volume of spheres with the cylinder volume.
I have filled up a die with sphere particles and weighted the content afterwards (n=7). Counting the particles and comparing it to the mass, gives me the knowledge that a similar experiment must have had an average of 188,7 particles with std = 2.2 particles.
85% of the particles, or above, have a diameter in the range of 1000-1400 µm.
Plotting the average diameter of the particles against the packing gives me this:
Would it be fair to say that another sample with average in region 1 and 4, and a qual or smaller STD, would be detected by my experiment____?