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Standard deviation is calculated by finding the squared differences from the mean, averaging those squared differences to get the variance, and then taking the square root of the variance. The formula is $\sigma = \sqrt{\frac{\sum (X - \mu)^2}{N}}$.

Conditions: The mean $\mu$ of the dataset has already been calculated.

Standard deviation is calculated by finding the squared differences from the mean, averaging those squared differences to get the variance, and then taking the square root of the variance. The formula is $\sigma = \sqrt{\frac{\sum (X - \mu)^2}{N}}$.

Conditions: The mean $\mu$ of the dataset has already been calculated.

The Z-score formula normalizes the data by subtracting the mean ($\mu$) from the original value ($X$) and dividing the result by the standard deviation ($\sigma$). The formula is $X' = \frac{X - \mu}{\sigma}$.

Conditions: Requires knowing the mean and standard deviation of the dataset

The Z-score formula normalizes the data by subtracting the mean ($\mu$) from the original value ($X$) and dividing the result by the standard deviation ($\sigma$). The formula is $X' = \frac{X - \mu}{\sigma}$.

Conditions: Requires knowing the mean and standard deviation of the dataset

The Z-score technique is best applied when the data is normally distributed, forming a proper bell-shaped curve. It aims to transform data into a distribution with mean 0 and standard deviation 1.

Conditions: The data is normally distributed.

The Z-score technique is best applied when the data is normally distributed, forming a proper bell-shaped curve. It aims to transform data into a distribution with mean 0 and standard deviation 1.

Conditions: The data is normally distributed.