depends on how your sample size is related to the power. If you know the exact relation you can find the impact.

Hi. Thanks for responding. the power was at 80%. Sample size was estimated at 79/group. there were only 2 treatment arms. A total of 240 subjects were randomized, so the sample size ended up being approximately 120 subjects/group. What would be the impact of this?

There are several ways to interpret this question. I will assume that you are generally familiar with the effect of sample size on power, accuracy, specificity, type I and type II errors, and so forth. You would have had to go through this when planning the original sample size.

There are many good things that happen with increased sample size. There is one bad aspect to increased sample size. With the larger sample size you are able to detect smaller treatment effects. So you can find a statistically significant difference that is too small to be biologically meaningful. However, there is seldom enough information about the system being studied to be able to balance the treatment effect against all other sources of biological variability. Typically biological experiments in the lab are too controlled, and this can result in unexpected field results following promising laboratory experiments.

Maybe you want to quantify the benefit realized from increasing the sample size. In this activity I would resample the data and run the analysis with several thousand random selections of 79. You now have a distribution of what might have been the outcome if you had stopped at 79 replicates. On this graph plot the results using all the data to show the effect of having increased the sample size. A less powerful approach would be to compare the results from the first 80 to the results using all the data. Had things worked differently, one would presume that you would have simply stopped with the first 80 in the original design.

In terms of formulating the null hypotheses, increasing sample size will have no effect. However, sometimes increasing the sample size can enable you to look at factors that you would have had to ignore with the smaller sample size. Are there differences between females and males, ethnic differences, age, weight, and so forth.

Did you means something else?

Thanks Timothy. That makes sense. What is the one bad thing that happens with increased sample size? Did I miss it in the text above?