This time your samples are all coming from the same population of cultures, presumably all identical except that half of them were sampled at one time and half at the other time. All of the data points are linked by the fact that they were obtained from cultures from a common source. However, there is no special one to one correspondence between any one data point in one set and a unique data point in the other. There is no basis for a paired t test, so we must run a test for independent samples.
The assay itself is the variable in this example. If the assay was 100% accurate and reliable, we would only have needed to look at one sample from a given cell line, and maybe repeat it once. Even a highly significant difference should be considered a preliminary result until the experiment can be repeated successfully at least once or twice.
Recall case study #1. If the t test returned a probability (p) value of 0.96 does this mean that there was a significant difference and the drug was effective?
In case study #2 the treated group actually showed an average weight gain. The t test returned a p value < 0.001. What is your conclusion?
Suppose that in case study #3 the average half life of hormone-treated cultures was 12 hours longer than the average half life of untreated cultures. The t test returned a p value of 0.33. What is your conclusion?
Would you be correct in stating that the result is significant or insignificant?
Suppose that in case study #4 average superoxide dismutase activity was 30% lower after 10 days than it was in the beginning. The t test gave a p value of 0.07. What result do you report? It turns out that this study is very important, and if you indeed find that a decline in superoxide dismutase activity is a primary cause of cell death then good things will happen to your career. How will you proceed from this point?