1. statistics Specific number
numerical measurement determined by a set of data
Example: Twenty-three percent of people polled believed that there are too many polls.

2. Statistics Method of analysis
a collection of methods for planning experiments, obtaining data, and then then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data
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3. Definitions Population
the complete collection of all elements (scores, people, measurements, and so on) to be studied. The collection is complete in the sense that it includes all subjects to be studied.

4. Definitions Census Sample
the collection of data from every element in a population
Sample
a subcollection of elements drawn from a population
Emphasize that a population is determined by the researcher, and a sample is a subcollection of that pre-determined group. For example, if I collect the ages from a section of elementary statistics students, that data would be a sample if I am interested in studying ages of all elementary statistics students. However, if I am studying only the ages of the specific section of elementary statistics, the data would be a population.

5. Definitions Parameter population parameter
a numerical measurement describing some characteristic of a population
population
parameter

6. Definitions Statistic sample statistic
a numerical measurement describing some characteristic of a sample
sample
statistic

7. Definitions Quantitative data
numbers representing counts or measurements
Qualitative (or categorical or attribute) data
can be separated into different categories that are distinguished by some nonnumeric characteristics

8. Definitions Quantitative data
the incomes of college graduates
Qualitative (or categorical or attribute) data
the genders (male/female) of college graduates

9. Definitions Discrete Continuous
data result when the number of possible values is either a finite number or a ‘countable’ number of possible values
0, 1, 2, 3, . . .
Continuous
(numerical) data result from infinitely many possible values that correspond to some continuous scale that covers a range of values without gaps, interruptions, or jumps
Understanding the difference between discrete versus continuous data will be important in Chapters 4 and 5.
When measuring data that is continuous, the result will be only as precise as the measuring device being used to measure. 2 3

10. Definitions
Discrete
The number of eggs that hens lay; for example, 3 eggs a day.
Continuous
The amounts of milk that cows produce; for example, gallons a day.

11. Definitions nominal level of measurement
characterized by data that consist of names, labels, or categories only. The data cannot be arranged in an ordering scheme (such as low to high)
Example: survey responses yes, no, undecided, zip codes, color of an M&M
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12. Definitions ordinal level of measurement
involves data that may be arranged in some order, but differences between data values either cannot be determined or are meaningless
Example: Course grades A, B, C, D, or F, Military Rank
Understanding the differences between the levels of data will help students later in determining what type of statistical tests to use.
Nominal and ordinal data should not be used for calculations (even when assigned ‘numbers’ for computerization) as differences and magnitudes of differences are meaningless.

13. Definitions interval level of measurement
like the ordinal level, with the additional property that the difference between any two data values is meaningful. However, there is no natural zero starting point (where none of the quantity is present)
Example: Calendar Years, Temperature
Students usually have some difficulty understanding the difference between interval and ratio data. Fortunately, interval data occurs in very few instances.

14. Definitions ratio level of measurement
the interval level modified to include the natural zero starting point (where zero indicates that none of the quantity is present). For values at this level, differences and ratios are meaningful.
Example: Height, Weight, Number of Credits

15. Levels of Measurement Nominal - categories only
Ordinal - categories with some order
Interval - differences but no natural starting point
Ratio - differences and a natural starting point
review of four levels of measurement

16. Methods of Sampling Random Systematic Convenience Stratified Cluster
review of the 5 different types of sampling

17. Definitions Random Sample Simple Random Sample (of size n)
members of the population are selected in such a way that each has an equal chance of being selected
Simple Random Sample (of size n)
subjects selected in such a way that every possible sample of size n has the same chance of being chosen

18. Random Sampling - selection so that each has an equal chance of being selected
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19. Systematic Sampling - Select some starting point and then select every K th element in the population

20. Stratified Sampling - subdivide the population into subgroups that share the same characteristic, then draw a sample from each stratum

21. Cluster Sampling - divide the population into sections (or clusters); randomly select some of those clusters; choose all members from selected clusters
Students will most often confuse stratified sampling with cluster sampling. Both break the population into strata or sections. With stratified a few are selected from each strata. With cluster, choose a few of the strata and choose all the member from the chosen strata.

22. Convenience Sampling - use results that are readily available
Hey!
Do you believe
in the death
penalty?

23. Definitions Sampling Error Nonsampling Error
the difference between a sample result and the true population result; such an error results from chance sample fluctuations.
Nonsampling Error
sample data that are incorrectly collected, recorded, or analyzed (such as by selecting a biased sample, using a defective instrument, or copying the data incorrectly).
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