1judging whether result is STATISTICALLY SIGNIFICANT just by looking at it
intuitive null hypothesis testing
2sampling types
random sample, convenience sample, and snowball sample
3individuals chosen RANDOMLY from population, almost NEVER used in psych research
random sample
4people chosen because they are "convenient", almost ALWAYS used in psych research
convenience sample
5people chosen, usually from convenience sample, and they send suvery to EVERYONE THEY KNOW. similar to convenience sample
snowball sample
6different random samples from same population will produce DIFFERENT RESULTS, no "reason" for this, its just random variation
sampling error
7used for INFERENTIAL STATS, focuses on simple binary decision, does NOT tell us how BIG the DIFFERENCE is or how STRONG a correlation is
null hypothesis testing (NHT)
8if i think theres SOME relationship in population, then sample result is
real
9if i think theres NO relationship in population, sample result is
due to chance
10theres NO REAL RELATIONSHIP in population, theres no STATISTICALLY SIGNIFICANT RELATIONSHIP, we either REJECT or FAIL to reject it, NEVER accept it. its the STATUS QUO.
null hypothesis (Ho)
11there IS REAL RELATIONSHIP in population. there IS a STATISTICALLY SIGNIFICANT RELATIONSHIP, its what we are TESTING that there IS a relationship.
ALT. hypothesis (H1)
12means that there IS a relationship, our results BACK UP what we though would happen ( H1 is "correct")
rejecting NH
13means that theres NO relationship, results do NOT back up what we thought would happen, status quo REMAINS. ( H0 is "correct")
failing to reject NH
14STRONGER relationships more likely to reflect REAL relationships in population
relationship strength
15results based on LARGER samples are more likely to reflect REAL relationships in population
sample size
16DETERMINES if relationship is STATISTICALLY SIGNIFICANT or not
p-value
17sample result is STRONG ENOUGH and based on LARGE enough samples to convince us that there IS a REAL relationship in population, result DID NOT just happen by CHANCE, does NOT mean that the results are big
statistically significant
19if p>0.05
fail to reject null
20we are 95% confident in our NULL HYPOTHESIS test results
95% confidence interval
21your sample may NOT be representative of the population
5% sampling error
22note about very large samples
relationship strength and sample size BOTH matter, a VERY LARGE value of one can COMPENSATE for a small value of other, BEWARE of "significant" results in studies with VERY LARGE SAMPLES.
23tells us the direction of the relationship, (positive or negative) and the strength (weak/small, medium, strong/large). a significant relationship does not mean it’s large.
effect sizes (cohens d, pearsons r)
24 Used to make an educated guess about what is true in the population
inferential stats