hypothesis testing: A better fly killer Say I have to prove that a new fly killer is better than an existing one and I set up an experiment where I put both fly killers in a room of live flies and empty and count how many flies are killed by each fly killer every 10 minutes.
The fly killers don't interfere; the past number of flies killed does not affect the number of flies available for killing.
If I have counts of flies killed by each fly killer every 10 minutes, I can work out mean fly kills for each product and compare them, but how do I determine that I have enough samples to work out if any difference is significant?
What if I calculate standard deviations of the means for each fly killer, Can I use these to make a better statement of the validity of any statement that one is better than another?
I should just state that my statistical knowledge is not great, I am an engineer that found the term "hypothesis testing" just today when doing my own search.
Thanks.

First, read up on the basics of hypothesis testing, i.e., Null vs Alternative Hypotheses, the role of the test statistic and rejection region, Type I vs Type II error. After that, you will want to model the number of flies caught every 10 minutes as a Poisson random variable with mean rate $\lambda$ flies per 10 minutes. Then, I think this post will answer your questions; https://stats.stackexchange.com/questions/9561/checking-if-two-poisson-samples-have-the-same-mean
