1. Statistics Workshop Tutorial 1
Fundamentals

2. Definitions
Data
observations (such as measurements, genders, survey responses) that have
been collected.
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3. Definitions Statistics
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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4. 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.
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5. Definitions Census Sample
the collection of data from every member of the population.
Sample
a sub-collection of elements drawn from a population.
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6. Definitions Descriptive Statistics Inferential Statistics
summarize or describe the important characteristics of a known set of population data
Inferential Statistics
use sample data to make inferences (or generalizations) about a population
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7. Definitions Parameter population parameter
a numerical measurement describing some characteristic of a population
population
parameter
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8. Definitions Statistic sample statistic
a numerical measurement describing some characteristic of a sample.
sample
statistic
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9. Definitions Quantitative data
numbers representing counts or measurements.
Example: weights of supermodels.
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10. Definitions Qualitative (or categorical or attribute) data
can be separated into different categories that are distinguished by some nonnumeric characteristics.
Example: genders (male/female) of professional athletes.
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11. Working with Quantitative Data
Quantitative data can further be distinguished between discrete and continuous types.
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12. Definitions Discrete Example: The number of eggs that hens lay.
data result when the number of possible values is either a finite number or a ‘countable’ number of possible values.
0, 1, 2, 3, . . .
Example: The number of eggs that hens lay.
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13. Definitions 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. 2 3
Example: The amount of milk that a cow produces; e.g gallons per day.
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14. Key Concepts
Sample data must be collected in an appropriate way, such as through a process of random selection.
If sample data are not collected in an appropriate way, the data may be so completely useless that no amount of statistical torturing can salvage them.
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15. Definitions Random Sample Simple Random Sample (of size n)
members of the population are selected in such a way that each individual member has an equal chance of being selected
Simple Random Sample (of size n)
subjects selected in such a way that every possible sample of the same size n has the same chance of being chosen
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16. Definitions Sampling Error
the difference between a sample result and the true population result; such an error results from chance sample fluctuations.
Non Sampling 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)

17. Bias
Bias is a term which refers to how far the average statistic lies from the parameter it is estimating, that is, the error which arises when estimating a quantity. Errors from chance will cancel each other out in the long run, those from bias will not.

18. Now we are ready for
Part 4 of Day 1