Full summary Correlational Reserach Methods

,
,
, Factors influenang Om

If X remains equal and N increases, Om decreases /up until v 1000)
· =




=larger sample size yields smaller estimation errors
IfN is constant, And O of scores increases Om increases
↑
.




↳small Om is good because it indicates that M scores are close , to M ,
so less

Prediction error ,
so magnitude of sampling error is small




Distance from M to M
· location of a
single X score
by computing a z-score




(M
32 =




· location of sample mean Example :



2 N = 130 M = 73 76 . 0 7 062
=
.
population
n= 9 M 76 22
=
.
sample



Among .354
2
z-values above 1 96 .




are considered
-if do not have we O ,
use SD Question : is M far from M ? far fram M
=SEm=


6.
->
for 95% confidence interval , no



characteristics
=same
formula , same

↳ But it does have 2 errors ; SD & M
the t distribution (or when sample is small
so : if O is not known ,
we use




t =
M
-
-

Mo ->
M = No + toSEm
SEM




Tdistributions ↓
degrees of freedom :
n 1-




-> the
larger of ,
the more normal the curve

->
When af >120 ,
the sample error of SD becomes negligible
= close to o = known


note af D =
normal distribution

=Middle 95% with increasing of