1. STATISTICS STATISTICS Numerical data

2. How Do We Make Sense of the Data? descriptively Researchers use statistics for two major purposes: (1) descriptively to characterize measurements made on groups or individuals inferentially (2) inferentially to judge whether these measurements are the result of chance

3. Statistics are used to makes sense of the data Frequency Distribution:Frequency Distribution: A chart showing how frequently each of the various scores in a set of data occurs Frequency Distribution organizes the raw data, making it more easily interpreted

4. Histogram A graphical display of tabulated frequencies, shown as bars

5. Bar Graphs

6. Descriptive Statistics Numbers that describe the main characteristics of the data Statistical procedures used to describe characteristics and responses of groups of subjects Basic Purpose: To observe and record behavior

7. Describing the Data With Descriptive Statistics Descriptive statistics include: The mean The median The mode The range The standard deviation The normal distribution

8. Measures of Central Tendency Central Tendencies are Averages The central point around which the scores seem to cluster Mean, Median, Mode, Range All are descriptive statistics and measures of central tendency All are descriptive statistics and measures of central tendency

9. : the of a distribution The Mean: the average of a distribution (as in a mean GPA) (as in a mean GPA) : in a distribution The Median: the middle score in a distribution (Think Med as Mid…as in middle) (Think Med as Mid…as in middle) the score in a distribution The Mode: the most often occurring score in a distribution (Mode sounds like ‘Most’) (Mode sounds like ‘Most’)

10. The Range: The difference between the lowest and highest scores in a distribution

12. Median Best measures of central tendency to examine data 1. Less resistant to extreme scores than the mean 2. More reflective of the data than the mode Ex: pay scales, salary, house costs

13. The Mean The measure of central tendency that is most affected by extreme scores

14. The Standard Deviation The Standard Deviation The average difference between the scores and their meanThe average difference between the scores and their mean Takes into account all of the scoresTakes into account all of the scores

15. The Normal Distribution A bell shaped curve, describing the spread of a characteristic throughout a population

16. The Normal Distribution 68.26

17. Correlation A relationship between two variables, in which changes in one variable are reflected in changes in the other variableEXAMPLE A child’s height  age Education  income

18. Correlation Coefficient A number between -1 and +1 expressing the degree of relationship between two variables Does NOT tell about cause and effect Simply gives information on the direction and strength of the relationship

19. Positive Correlation If people have high scores on one variable also have high scores on another, the correlation is positive The Correlation Coefficient is also positive ex: +.09

20. If there is a positive correlation between the number of children a person has and overall life satisfaction, we would find that people with more children… ….are more satisfied with their lives than are people with fewer children.

21. Negative Correlation Negative Correlation If people have high scores on one variable, but low on another, the correlation is negative The Correlation Coefficient is also negative…(less than 0) The Correlation Coefficient is also negative…(less than 0)

22. If a study finds that there is a negative correlation between exercise and blood pressure, this would most likely indicate that…. …people who exercise more tend to have lower blood pressure.

23. Zero Correlation Zero means there is no relationship between scores

24. If you measured the height of each student in class and the amount of money that each person has in his or her pockets, you would expect to find…. …a zero correlation.

25. Making Inferences with Inferential Statistics Inferential statistics are used to assess whether the results of a study are reliable or whether they might be simply the result of chance Often used to determine (or ‘infer’) whether two or more groups are essentially the same or different

26. Sampling Random Sample A sample group of subjects selected by chance Representative Sample A carefully selected sample of a targeted group, such as customers, whose characteristics represent (as accurately as possible) the larger population Example: A Gallup Poll….only a few hundred participants provide an accurate assessment According to a Gallup poll done at the end of the 20 th century, about one- third of Americans believe aliens have visited us, an increase of 5% over the previous decade.

27. Statistical Significance Results of an experiment must reach the level of Statistical Significance to ensure the results are not due to chance Psychologists accept a difference between groups to be ‘real’ or significant when the probability that in might be due to chance is less than 5 in 100

28. Statistical Significance When a statistic is significant, it simply means that you are very sure that the statistic is reliable. It doesn't mean the finding is important p<.05 = chance of very little influence p<.01 = (less 1 in 100) more strict p<.001 = (less 1 in 1000) more strict Significance levels of at least.05 is sought….01 is better

29. Question: If a study resulted in a large p value (such as p>.50), what could we say about the results? Answer: There is a high probability that we received these results just by chance.

30. End of Chapter 2