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1. There are two primary techniques used in today’s statistical world

a. Descriptive techniques describe and summarize data

b. Inferential techniques allow us to estimate and draw conclusions about populations from samples

2. Data can be formally categorized in three categories

a. Interval or real numbers

b. Nominal or categorical

c. Ordinal or ratings

3. Data can be collected through a variety of techniques but quality of sample (should you need to sample data) plays a major role in validity of the process and result. Garbage-in-Garbage-out is applicable in statistical analysis that in any other case

4. Statistical inference is primarily derived from sample distribution (more to come on distributions in later blog post)

5. Hypotheses testing patterns are similar across various techniques – Remember Type I and Type II errors (always)

6. Hypotheses and hence decisions always will contain type I and type II errors.  Hypotheses rejections are determined by probability of type I errors.

7. When analyzing interval data, attempt to explain as much variation as possible

8. Nominal data requires clear definition of the categories and categorization of data

9. Ranking procedures are most often employed for ordinal data

10. Data gathered by experimentation are often more reliable in achieving definitive interpretation and observational data.

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