1. Descriptive vs. inferential statistics
• Descriptive – used to describe an existing population
• Inferential – used to draw conclusions of related populations

2. Graphical descriptions – histograms, frequency polygons/curves, pie charts

3. Measures of central tendency
• Mean – average – used most often
• Median – midpoint value – used when data is skewed
• Mode – most frequently occurring value – used when interested in what most people think

4. Measures of variability
• Range – highest value minus lowest value
• Standard deviation – average of how distant the individual values are from the mean

5. Normal curve
• Bell shaped curve – 68% of values lie within one standard deviation of the mean
• Non-normal – skewed either negatively (tail to left) or positively (tail to right)
• Percentiles  - values that fall between two percentile values
• Standard scores – distance from mean in terms of the standard deviation – z = (X-m) / s.
• Z scores – transformed standard scores – Z = 10z + 50

6. Variables
• Quantitative – things that can be measured (age, income, number of credits)
• Qualitative – things without an inherent order (college major, address)

7. Populations and samples
• Population – entire universe from which a sample is drawn
• Sample – subset of population
• Symbols – mean m, µ; standard deviation s, ?; variance s2, ?2

8. How representative is the sample
• Random sample – use random numbers to choose members of the sample
• Stratified sample – sample that represents subgroups proportionally

9. Hypothesis testing
• Hypothesis as to relationship of variables – similar or different
• Inference from a sample to the entire population

10. Statistical significance
• Accept true hypotheses and reject false ones
• Based on probability (10 heads in a row occurs once in 1024 coin tosses)
• Significant result means a significant departure from what might be expected from chance alone
• Example – a result two standard deviations from the mean occurs on 2.3% of the time in a normally distributed population
11. Null hypothesis
• Assumption that there is no difference between two variables
• Example – Male and female college students do similar amounts of music downloading using KaZaa
• Example – School use of computers is unrelated to income of the students’ families

12. Levels of significance
• 5 percent level – Event could occur by chance only 5 times in 100
• 1 percent level – Event could occur by chance only 1 time in 100
• Significance level should be chosen before doing experiment

13. Types of errors
• Type I error – Rejection of a true null hypothesis
• Type II error – Acceptance of a false null hypothesis
• Decreasing one type increases the other

14. One and two tailed tests
• One tailed test – Experimental values will only fail the null hypothesis in one direction
• Two tailed test – Values could occur on either the positive or negative tail of the curve

15. Estimation
• Concerns the magnitude of relationships between variables
• Hypothesis testing asks “is there a relationship”
• Estimation asks “how large is the relationship”
• Confidence interval – provides an estimate of the interval that the mean will be in

16. Sequence of activities
• Description
• Tests of hypotheses
• Estimation
• Evaluation

17. Correlation
• Quantifiable relationship between two variables
• Example – relationship between age and type of computer games played
• Example – relationship between family income and speed of home computer

18. Correlation chart
• Two (or more) dimensional table
• Variables on the axes, could be intervals
• Scattergram – positive correlated values scatter with positive slope, negative with negative slope

19. Product-moment coefficient
• Formula based on deviations from means
• If deviations are the same or similar, values are positively correlated
• If deviations are the opposite, values are negatively correlated
• Most correlations are somewhere in between +1 and -1