confounding variables
a variable that affects both your independent variable (X) and dependent variable (Y) in a way that creates a false or distorted relationship between them.
p value less or equal than a
reject Ho
p value greater than a
fail to reject Ho
type 1 error (a)
rejecting a true null hypothesis/ false positive
type 2 error (B)
failing to reject a false null hypothesis/ false negative
power
probability that a random sample will lead to rejection of a false null hypothesis// power= 1-B
what effects power?
1- sample size
2-effect size (how far is the null to the truth )
3-rate of a(type 1)
how to reduce bias?
1-randomization: units that are otherwise identical are assigned to be controls or treatments
2-use blinding: nobody knows if they are in treatment or control group
3-have a control group: group that doesn’t receive treatment (provide baseline of comparison)
how to decrease sampling error?
1-use replication: application of treatment to multiple, independent experimental subjects or units
2-ensure balance: equal number of units in the treatment and the control
3-use blocking: divide experimental units into groups with known confounding variables
4- implement extreme treatments
single blind ?
when the participants don’t know if they are in control or treatment group
double blind?
when neither the participants and researchers know who is in control or treatment group
pseudo replication
counting multiple measurements of the same experimental unit as if they were multiple independent samples.
extreme treatments
a treatment that you may add to an experiment to see if doing more of a treatment will elicit more or less of an effect
