# Machine Learning Statistics

Statistics are tools to get answers to questions about data:

• What is Common?
• What is Expected?
• What is Normal?
• What is the Probability?

## Inferential Statistics

Inferential statistics are methods for quantifying properties of a population from a small Sample:

You take data from a sample and make a prediction about the whole population.

For example, you can stand in a shop and ask a sample of 100 people if they like chocolate.

From your research, using inferential statistics, you could predict that 91% of all shoppers like chocolate.

## Incredible Chocolate Facts

Nine out of ten people love chocolate.

50% of the US population cannot live without chocolate every day.

You use Inferential Statistics to predict whole domains from small samples of data.

## Descriptive Statistics

Descriptive Statistics summarizes (describes) observations from a set of data.

Since we register every newborn baby, we can tell that 51 out of 100 are boys.

From these collected numbers, we can predict a 51% chance that a new baby will be a boy.

It is a mystery that the ratio is not 50%, like basic biology would predict. We only know that we have had this tilted sex ratio since the 17th century.

## Note

Raw observations are only data. They are not real knowledge.

You use Descriptive Statistics to transform raw observations into data that you can understand.

## Descriptive Statistics Measurements

Descriptive statistics are broken down into different measures:

Tendency (Measures of the Center)

• The Mean (the average value)value
• The Median (the mid point value)
• The Mode (the most common value)

Spread (Measures of Variability)

• Min and Max
• Standard Deviation
• Variance
• Skewness
• Kurtosis

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