Contrary to popular belief, there is no single perfectly correct approach. Some choices are wrong, but that leaves a suite of options each with strengths and weaknesses. This is no longer a test in introductory statistics class.

There is a serious flaw in the data where one group consists of two observations. Any analysis will have to find ways to mitigate this issue. One option is to form a few larger groups based on some shared characteristics. Rather than study Pasimachus elongatus, look at genus, family, or order level interactions.

Ask specific questions of the data. Do not take a pile of numbers and attempt to figure out if there are any relationships. This is a back and forth activity wherein the biology of the system informs on the statistical methods and the statistical methods help with understanding the biology. This is a give-and-take process as often the biological constraints limit statistical options and the statistical options limit the biological conclusions. Often the best solution is to carefully gather more data, but as often financial and time constraints prevent implementation of this option.

Simple option is to look at individual comparisons. Note that the larger the number of statistical tests the more likely you are to find at least one significant result. This is an artifact. Correct using something like a Tukey test for multiple comparisons or a Bonferroni correction where the critical level of significance (usually 0.05) is divided by the number of tests to get a corrected critical value.

Try methods like Factor Analysis, Cluster Analysis, or Discriminant Analysis for a better picture of overall patterns. The specific method is determined by the way that you ask questions. However, such methods will drop any observation with missing data, and they are sensitive to small sample sizes.

When JMP gives you output that you do not understand (look at all of the output, not just what you think you need) then go to the SAS STAT User manual for an explanation. The SAS technical manuals do a good job of discussing what the output means and why it is important to see that output. JMP is a front end for SAS.

https://support.sas.com/documentation/cdl/en/statug/63962/HTML/default/viewer.htm#titlepage.htm and go to "procedures" tab on the left. Alternatively, do an internet search for the unfamiliar term and add "SAS" to the search list.

Hi, Natalie!

I don't know if I got well your question, but you can compare multiple groups by conducting a one way ANOVA test (a means comparison test) - for different and independent samples (e.g., a sample received a treatment and the other didn't). If you are comparing two groups (e.g., male and female), you can run a T Test (another means comparison test) - also for different and independent samples. Now, if you want to compare, for example, the time spending before and after a treatment (one single sample), you must run a T test for paired samples.

I don't know if this answer your question, but I hope I can help you somehow.

Best.