PSYC 204 Lecture 11: PSYC STATS 11
Document Summary
1- special case of continuous probability density function = normal distribution. Normal distribution = continuous probability distributions for p = & q = as n increases. Ex: tossing coing where n= 2 for 2 trials of coin toss & equal p = & q = in 2 trials in 6 trials in 8 trials in 10 trials. *with more trials = more symmetrical & smoother distribution *smaller step increase. When n increase to almost infinity = get almost continuous & normal distribution. If binomial distribution = tried infinitely n of trials. As n increases = binomial distribution p = q = approaches normal. No matter what the variable always = 0 & = (cid:1005) & area = (cid:1005) *mapping of standard scores = sum of area is 1. Practical example ex: your company packages sugar in 1 kg bags. When you weigh sample of bags you get these. Normal distribution so mode/mean/median at same point.
