Hi Everyone, <br><br>I have been a bench scientist for about 20 years now. I really enjoy seeing people who are not professionally involved with science, engage in scentific studies of their own interest. <br><br>I wanted to offer you some tips so you won't get too many arguments against your inventions, and your experimental designs. One of the most basic criticisms a scientist would make against your experiments is that your measurements may be random, because you only measure once. <br><br><ol type="1"> <br><li>Take at least 3 measurements for each of your data points. (for instance if you want to compare theraband gold against theraband blue, you would want to take chono measurements three times for each band shooting under the same conditions each time)<br> </li> <li>Calculate a mean and standard error of your measurements for each data point. Scientists like to report data for a small number of data points as mean +/- standard error (mean +/- SE). SE will be the average error (a measure of variability) for the means for your data set. If you use excel this can be retrieved by using the descriptive statistics tool.<br> </li> <li>Use a statistical test to determine if your experimental groups are statistically different. In excell there are three tests that are handy: t-test assuming equal variance, t-test assuming unequal variance, and paired t-test. The variance as the name implies is a more crude measure of variability of your data set. The variance might be equal if you are comparing two materials of similar composition, but may be unequal if one of the materials is different than the other. The paired t-test is to compare a group against a similar group that has had some intervention. For instance if you wanted to compare the same length therabands against a thereband that you had punched some holes into. The t-test in excell will do the necessary calculations to make the comparison, if your comparison has acheived 'statistical significance' the p-value will be less than 0.05. The smaller this number is the better, and basicly this number describes the chance that this is a real comparison versus a random one.<br> </li> <li>Display your data in a graph correctly. For most things you can display your data using bar graphs, with error bars indicating the standard error that was calculated initially from your means. After some practice you will be able to tell when you have achieved statistical significance by looking at the SEs on your bar graphs for the various comparisons you wish to make. Time Courses, and dose responses, experiments which your modulating some variable incrementally are best done with line graphs, where each data point is displayed as a point,mean +/- SE<br> </li> </ol> <br>Anyhow I didnt want to go into great detail, but I hope may even make your experiments more fun, because you are using the methods scientists use.