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We need to find that what is outlier.

Outlier is an extremely high or extremely low data point relative to the nearest data point and the rest of the neighboring co-existing values in a data graph or dataset you're working with. These are stragglers extremely high or extremely low values in a data set that can throw off our stats. For example, if someone were measuring children's nose length, our average value might be thrown off if Pinocchio was in the class.

We need to find that how can we identify potential outliers, using only the first and third quartile .

We will use interquartile rule to find potential outliers.

1. Calculate the interquartile range for the data.
2. Multiply the interquartile range $\left(IQR\right)$ by $1.5$ (a constant used to discern outliers).
3. Add $1.5x\left(IQR\right)$ to the third quartile. Any number greater than this is a suspected outlier.
4. Subtract$1.5x\left(IQR\right)$ from the first quartile. Any number less than this is a suspected outlier.