statistical significance and intuitive null hypothesis testing

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1
judging whether result is STATISTICALLY SIGNIFICANT just by looking at it
intuitive null hypothesis testing
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sampling types
random sample, convenience sample, and snowball sample
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individuals chosen RANDOMLY from population, almost NEVER used in psych research
random sample
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people chosen because they are "convenient", almost ALWAYS used in psych research
convenience sample
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people chosen, usually from convenience sample, and they send suvery to EVERYONE THEY KNOW. similar to convenience sample
snowball sample
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different random samples from same population will produce DIFFERENT RESULTS, no "reason" for this, its just random variation
sampling error
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used 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)
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if i think theres SOME relationship in population, then sample result is
real
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if i think theres NO relationship in population, sample result is
due to chance
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theres 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)
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there IS REAL RELATIONSHIP in population. there IS a STATISTICALLY SIGNIFICANT RELATIONSHIP, its what we are TESTING that there IS a relationship.
ALT. hypothesis (H1)
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means that there IS a relationship, our results BACK UP what we though would happen ( H1 is "correct")
rejecting NH
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means that theres NO relationship, results do NOT back up what we thought would happen, status quo REMAINS. ( H0 is "correct")
failing to reject NH
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STRONGER relationships more likely to reflect REAL relationships in population
relationship strength
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results based on LARGER samples are more likely to reflect REAL relationships in population
sample size
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DETERMINES if relationship is STATISTICALLY SIGNIFICANT or not
p-value
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sample 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
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if p<0.05
reject null
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if p>0.05
fail to reject null
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we are 95% confident in our NULL HYPOTHESIS test results
95% confidence interval
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your sample may NOT be representative of the population
5% sampling error
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note 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.
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tells 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)
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Used to make an educated guess about what is true in the population
inferential stats
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