2. Research: Analyzing the Data Now that data has been gathered from a correlational, descriptive, or experimental research method, it’s time to analyze it! Off the top of your head estimates are often misleading. Big, round, undocumented numbers are misleading and should be investigated! –Ex: One percent of Americans-2.6 million-are homeless. (Or is it 300,000, as estimated by the government?) –We ordinarily use only 10 % of our brain. (Or is it closer to 100%? Which 90% would you be willing to sacrifice?)

3. Analyzing the data Measures of central tendency: help us summarize the data for quick analysis. Mode: most frequently occurring score Mean: Arithmetic average Median: The middle score

4. Analyzing the data A few abnormally large or small numbers can throw off the mean in statistical data. Always note which measure of central tendency is being reported.

5. Measures of Variation How similar or diverse are the scores in the data? Low variability= more reliability Range of scores: the gap between the lowest and the highest scores provides only a crude estimate of variation because a couple of extreme scores in an otherwise uniform group, such as $475000 and $710,000 will create a deceptively large range. Standard deviation: how much the scores deviate from one another.

6. When is a difference reliable? Representative samples better than biased samples. –Keep in mind what population a study has sampled Less-variable observations are more reliable than those that are more variable. –An average is more reliable when it comes from scores with low variability. More cases are better than fewer. –Averages based on many cases are more reliable (and less variable) than averages based on only a few cases. Don’t be overly impressed by a few anecdotes. Generalizations based on a few unrepresentative cases are unreliable.

7. When is a difference significant? Data must be reliable before being judged for their significance. Statistical significance: the sample averages are reliable and the difference between them is relatively large –i.e. the difference observed is probably not due to chance Remember: statistical significance indicates the likelihood that a result will happen by chance, it does not indicate the importance of the result.