1. Analysis of Distribution If the sample is truly random and there is no bias in the sampling then the expected distribution would be a smooth bell-shaped curve. However, factors can enter the sampling to affect the shape of the distribution curve. PopulationSample Random Sample Sample size > 30 for each sub-group Each sub-group has Equal numbers of individuals

2. Normal Distribution Curve

3. Task In this topic you will be trying to compare the sample distributions of two subgroups taken randomly form a population to determine whether there is enough evidence to answer you question and whether the sample trends will occur in the population also! PopulationSample Random Sample Sample size > 30 for each sub-group Each sub-group has Equal numbers of individuals

4. Mass Of Trout in South Taranaki Rivers Kaupokanui RiverWaingongoro River FREQENCY%FREQENCY% FREQENCY%FREQENCY% Mass In Grams

5. Describing Feature of the Distribution Clusters: Concentration of data around specific values Skewness: When the Median and Mean are not aligned Outliers: Values that lie outside the boundaries of the distribution

6. Summary Statistics Minimum Lower Quartile Median Upper Quartile Maximum Mean Standard Deviation

7. Skewness

8. Outliers An outlier is an observation that lies an abnormal distance from other values in a random sample from a population. In a sense, this definition leaves it up to the analyst (or a consensus process) to decide what will be considered abnormal. Before abnormal observations can be singled out, it is necessary to characterize normal observations.

9. Outliers An outlier is an observation that lies an abnormal distance from other values in a random sample from a population. In a sense, this definition leaves it up to the analyst (or a consensus process) to decide what will be considered abnormal. Before abnormal observations can be singled out, it is necessary to characterize normal observations.