I am trying to determine if the difference between the expected quantity and the actual quantity is significant using SPSS. I have been told I need to use a one-tailed t-test but I am having difficulty trying to ascertain which test is the appropriate test. My research involves quantities of bones to determine primary deposition (were they killed where they were found) or secondary deposition (were they killed elsewhere and relocated to where they were found?). We know the overall distribution of bones and in primary sites we expect the distribution of small bones of the hands and feet to be similarly distributed as the rest of the human skeleton. However, we find the representation of hand/foot bones to be very different from that expected quantity . It is obvious when you look at the raw numbers but I wish to analyze and prove the differences are significant. So... we have an expected quantity on which to base the comparison. We also have the actual quantity recovered. I am using the two numbers as variables in a variety of t-test analyses but I am getting no results, albeit likely due to my ignorance of the data needed to correctly run the tests. The one-sample t-test "cannot be computed because the sum of the caseweights is less than or equal 1". I don't know the term caseweights. The independent-samples t-test asks for a grouping variable but I know of no such grouping variable. The paired-samples t-test also gives the error message "cannot be computed because the sum of the caseweights is less than or equal 1".