Statistics
• Sample: Descriptive Statistics
• Population: Inferential Statistics
Descriptive Statistics
• Measures of central tendency
– Mean
– Median
– Mode
• Measures of variability
– Standard deviation (SD)
– Variance (SD squared)
• Correlation – measure of relatedness
Populations and Samples
• Population: all the possible scores
– All living humans
– All possible rolls of a pair of dice
• Sample: a subset of the population
– Random sample – best kind; unbiased
• Relationship of sample mean to population
mean – example: number of pets
Example of Comparing 2 Means
• DV = # of minutes balancing on one foot
• IV = whether blindfolded or not
• Hypothesis: Lack of visual cues decreases balance
• Means of two samples: 2.6 yes, 5.1 no
• Confirmed hypothesis?
– Is the difference due to chance?
– How can you tell if a difference found in an experiment
is due to chance?
– How often do differences this big occur by chance?
Tests of Significance
• t
• F
• Tell us how often we would get the results
we see in our sample just by chance
Hypothesis Testing
• H0 : the null hypotheis
– Mean1 = Mean2
– Experimental Condition = Control Condition
• H1 : the research hypothesis
– Mean1 does not equal Mean2
– Experimental Condition does not equal Control
Condition
Hypothesis Testing: Logic
• Assume H0
• Calculate a test statistic (such as t) to get p (p =
how often a difference this large would occur by
chance)
• If p .05, you can not reject H0
• Say “the difference was not statistically significant.”
Testing Hypotheses
• Are the means for the two groups really
different? (Blindfolded vs. not)
• Steps to Follow:
– Check for invalid data (analyze-> descriptive statistics ->
explore)
– Check for outliers; remove outliers
– Compare means with t-test (analyze -> compare means)
• Independent Samples: for 2 groups
• Paired Samples: for repeated measures (one group)