1. Statistics Sample: Descriptive Statistics Population: Inferential Statistics

2. Descriptive Statistics Measures of central tendency –Mean –Median –Mode Measures of variability –Standard deviation (SD) –Variance (SD squared) Correlation – measure of relatedness

3. 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

4. Example 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?

5. Tests of Significance t F Tell us how often we would get the results we see in our sample just by chance

6. Hypothesis Testing H 0 : the null hypotheis –Mean1 = Mean2 –Experimental Condition = Control Condition H 1 : the research hypothesis –Mean1 does not equal Mean2 –Experimental Condition does not equal Control Condition

7. 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, conclude that H0 is false (“reject H0”) –If you reject H0, you are left with H1 Conclude that the means are really different Say “the difference is statistically significant.” –If p >.05, you can not reject H0 Say “the difference was not statistically significant.”

8. 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)