1. Chapter 1 DATA AND PROBLEM SOLVING

2. Section 1.1 GETTING STARTED

3. Vocabulary  Statistics – The science of gathering, describing and analyzing data or the actual numerical descriptions of sample data.  Population – The particular group of interest.  Data – Information prepared for a study  Census – When data is obtained from every member of the population.  Parameter – A numerical description of a particular population characteristic.  Sample – A subset of the population from which data is collected.  Sample Statistic – The actual numerical description of a particular sample characteristic.

7. Two Types of Statistics  Descriptive Statistics –   Inferential Statistics -

8. Example 3 Descriptive

9. Example 4 Inferential

10. Section 1.2 LEVEL OF MEASUREMENT

11. Qualitative vs. Quantitative Labels or descriptions of traits of the sample  Categorical  Examples  Foods  Places  Colors  Identification Numbers Counts and measurements  Numerical  Examples  Test scores  Average rainfall  Median heights

13. Continuous vs. Discrete Data that can take any value within an interval  Cannot be counted  Examples  Measurements  Time  Temperature Data that refers to individual data that is countable  Can be counted  Examples  Number of pets

15. Levels of Measurement

17. Example 1 Qualitative Neither Nominal

18. Example 2 Quantitative Continuous Ratio

19. Section 1.3 THE PROCESS OF A STATISTICAL STUDY

21. Ways to Collect Data

22. Sampling Methods Random Every member of the population has an equal chance of being selected. Example: Drawing names from a hat. Stratified The population is divided into groups based on a characteristic, then members from each group are chosen randomly. Example: Separating students by class, then randomly choosing 5 from each class. Cluster The population is divided into groups that are similar to the population, then groups are chosen at random to sample. Example: Dividing students so there are 10 of each class in a group, then choosing 2 groups to sample.

23. Sampling Methods cont. Systematic The population is aligned in no specific order then every nth member is chosen. Example: Choosing every 5 th person. Convenience Choosing a sample the is “convenient” to the researcher. Example: Teacher surveying students from one of their classes to represent the population.

26. Types of Studies

27. Single-blind vs. Double-blind

28. Additional Vocabulary

30. Section 1.4 THE REALITY OF CONDUCTING A STUDY

31. Institutional Review Board  A group of people who review the design of a study to make sure that it is appropriate and the no unnecessary harm will come to the subjects involved.

32. Ethical and Practical Concerns Informed Consent Completely disclosing to participants the goals and procedures involved in a study and obtaining their agreement to participate. Biased A study tends to favor certain results Researcher Bias When the researcher influences the results of the study to favor a certain outcome.

33. Sampling Errors Errors resulting from the way the sample is chosen Dropout A participant who begins the experiment but then fails to complete it. Participation Bias When there is a problem with either the participation – or lack thereof – of those chosen for the study.

34. Non-sampling Errors Occur from sources other than the construction of the sample. Processing Errors A type of non-sampling error that occurs in the reporting, could be a typo in the data set.

35. Non-adheres Subjects who stray from the directions they were given, but remain in the sample to the end. Confounding Variables Variables that the researcher did not account for.