T-curves are offer (heavier failed) as sample size decreases. From an operative standpoint, how does that work to help us in our development of a Cl on the population mean? Flatter T-curves result in smaller T-values for the same Cl%, resulting in narrower Cl boundaries, which is beneficial O a That is not correct. As sample size goes down, the T-curves become more peaked. The resulting T-values are smaller than corresponding Z values and Ob therefore, not beneficial Less data means smaller sample sizes, which results in Cls with narrower boundaries As sample size goes down, we have less information to base our decisions on. The T-values get larger, making our CI's wider and giving us a better chance of Od still being right Smaller sample sizes are not a problem, as long as you always make an adjustment for degrees of freedom. The width of the Cl will be the same, regardless of Oe the sample size