# CRIM 320 Lecture Notes - Lecture 3: Skewness, External Validity, Internal Validity

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30 May 2013
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Crim 320 Week 3
Objectives
- Identify and correct violations of the assumption regarding normality of data
- Detect and remedy outliers
- Check the accuracy of your data
- Cope with missing data
- All of the above fall under diagnostics
Internal validity, measuring what you’re supposed
External validity = generizable to the greater population
Assumptions not met = results are garbage
T-tests = compare two averages, two means
Rely on the mathematical average being the best measure of central tendency
Information about skewness and kurtosis (see table in handout) i
Not trying to make variable perfectly normal, it just needs to be close enough. How do we determine
close enough
Lepto kurtosis = when the data is pushed up in the center, also is positive kurtosis
versus plateo kurtosis = pushed down, is also negative
95% CI = statistic± 1.96*(SE of statistic) Important formula
Example of 95% CI
- Skewness
- 95% CI =5.063 ± 1.96 *(0.241)
- =5.063 ± 0.472 (both add and subtract)
- =4.591 to 5.535 (produces a range, zero needs to falls within that range)
- Kurtosis
- 95%CI =30.197 ± 1.96 *(0.478)
- =30.197 ± 0.937
- =29.26 to 31.13
Remedies for skewness and Kurtosis, two solutions for this class
1. Converting to rates
2. Transformations
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