write participant method section of the scientific paper: We ran a An a priori sample size estimation using G*Power (Faul et al., 2007)power sample size analysis and this analysis indicated that you need 36 subjects, but due to reasons of counterbalancing. You were interested in I in a specific number of subjects. But at the end, he was only able to test 26 subjects due to reasons of time or something like that. And what you can do in the discussion part, you can have a look at the size. Or at the. Power you achieved with your sample size so. Now you have to use post hoc analysis and. The information are identical to that we. Put into the previous. We have to enter. Here 26 subject because it has the 26 subject. And here you can see. For your sample size, you have to power off yes coin. To identify and significant confluency effect or significant. Uh, effect of interruption in the medium size. At an alpha probability of .05. At the at the end, your plan was to have the power of .9 but. You didn't test the entire sample, so enough subject. Only 26 and with 26 six subjects had not the power of deployment 9, but of .8 and it would be interesting to replicate the experiment with an increased power. You can also run post hoc analysis into. Yes, determined the shift power with your sample size. So what I did was I was interested in the sample size view. We need to. Find an effect if the effect is consistent. And here you have different. Test types, but we can use the T test because. At the end, we assume that there is a confluency effect. Meaning that reaction times are higher in income. Front than the. Conference trials and this is the T test. Or we assume that. Reaction times are higher after an interruption than without interruption, so we. Have the T test. And we are interested in means. Between 2 dependent measures, so it's a repeated measures design, right? OK. And then you can. Fill in whether you have. The specific prediction meaning. That you know. Whether performance is worse in in concurrent or? Trials or whatever. This is a general prediction, meaning that you assume that there will be a difference, but you don't know whether congruent or incongruent. Trials will be better. So we have one tailed. The effect size here is the effect size according colons D. And an effect size of 0.5 would. Be a medium effect. And in effects in the medium size, this is the alpha probability. So we use a PV value of point. 05 and here you have the power. So. Test us the probability of find. And significant effect is the effect is exists so meaning. The power of. .90 indicates that in 10% of all your studies. You will not. Find this effect the significant effect. Even if it's existed. Yes. Yeah. So if you have the power of .8 this. Would mean if you have 100. Studies and 20% of the studies used your P. Value would be larger. .05. But in real life there is a difference. But your test is not able. To find this difference. OK. But we use the fire. Ohh .9. Now we can. And here you can see. We need the total sample size of 36 to achieve the power of .902 in order to identify effect with the size of 0.5 with an alpha level of point. 05