2. What is analysis
Explaining the one with the other
Association
Relationship: Linear vs Non linear
Comparison: Two or More Groups
Hypothesis Testing
3. Parametric vs Non Parametric Tests
◦ Parameter – Population measure
Mean, Standard Deviation, Proportion
◦ Statistic – Sample measure
Mean, Standard Deviation, Proportion
4. ◦ Nominal or Classificatory Scale
Gender, Locality, Religion, District, Block, State
Frequency, Mode,
◦ Ordinal or Ranking Scale
Beauty, Military Ranks, Product Preference
Median,
◦ Interval Scale
Celsius or Fahrenheit, Likert Scale, Rating Scale
Arithmetic Mean
◦ Ratio Scale
Kelvin temperature, Speed, Height, Mass or Weight,
Income, Expenditure, Age
Geometric Mean, Arithmetic Mean
5. Observations must be independent
Measurement at Interval or Ratio level
Observations drawn from Normally distributed
populations
Populations must have the same variances
Sampling: Random or Representative
6. Distribution Free tests
◦ No assumption of normality or homogeneity
No requirement of strong measurement
◦ nominal or ordinal level
Less powerful than parametric tests
Every parametric test has non-parametric counter
part
Parametric tests preferable when assumptions are
satisfied
7. Parametric Non-parametric
Assumed distribution Normal Any
Assumed variance Homogeneous Any
Typical data Ratio or Interval Ordinal or Nominal
Data set
relationships
Independent Any
Usual central
measure
Mean Median
Benefits
Can draw more
conclusions
Simplicity; Less
affected by outliers
8. Parametric Non-parametric
Correlation test Pearson Spearman
Independent
measures, 2 groups
Independent- t-test
Mann-Whitney test
Independent
measures, >2 groups
One-way ANOVA Kruskal-Wallis test
Repeated measures,
2 conditions
Matched-pair t-
test
Wilcoxon test
Repeated measures,
>2 conditions
One-way,
repeated
measures ANOVA
Friedman's test