# How to Interpret Z Scores Positive and Negative

A z score is a statistic that communicates how far a raw data point is from the mean. It is calculated by subtracting the sample mean from a raw score and dividing it by the sample standard deviation.

A z score can be negative or positive. A negative z score means that the raw data point is below the mean. A positive z score indicates that the raw data point is above the mean.

## Positive z-scores are above the mean

Z-scores are a way to interpret raw data values by comparing them to the population mean. When a score is positive, it means the value is above the mean; when it’s negative, it’s below the mean. You can find a z-score table online that calculates the probability or area of the distribution for each raw data value.

To calculate a z-score, you need the raw data value X, the population mean m, and the population standard deviation s. Using this formula, you can then find the z-score by dividing X by (m – s) / s.

A z score indicates how many standard deviations away your data point is from the mean. It can be either positive or negative, and the closer it is to zero, the more close to the mean it is. Z-scores are most commonly used with datasets that follow a normal distribution, but they can also be useful for data from other distributions.

## Negative z-scores are below the mean

When a data point is negative, it means that it is below the mean. To find the value of a negative z-score, look at the table below and subtract the area to the left from 1. This will give you the value of the z-score.

The z score shows where a data point lies on a normal distribution curve. It is a number that shows how many standard deviations above or below the mean it is. When the z-score is zero, it indicates that the data point is equal to the mean.

z scores can be used with any distribution, but they are most useful for identifying outliers in a data set. They can also be used to compare data points that have different characteristics, such as age and gender. For example, if a student has a higher z-score than another student, it may indicate that the first student performed better on the test. However, this conclusion cannot be made without comparing the individual students’ results.

## Positive z-scores are above the standard deviation

A z score is a measure of how many standard deviations a value lies above or below the mean. It can be calculated by subtracting the mean from a raw value and dividing it by the standard deviation. A positive z score indicates that the value is greater than the mean. A negative z score indicates that the value is below the mean.

Z scores are useful because they allow us to compare raw data sets that may have different means and standard deviations. They also help us to determine whether or not a value is unusual and possibly an outlier.

For example, imagine that the grades on a history midterm have a mean of 85 and a standard deviation of 2. A student named Michael scored 86 on the exam. To find the z score for this value, we can use a z-table. This table shows z values for any raw value that falls within the normal distribution.

## Negative z-scores are below the standard deviation

The z score of a value is a number that tells how many standard deviations it is above or below the mean, m. The positive z-score means that the raw score is higher than the mean, while the negative z-score indicates that the raw score is lower than the mean.

The formula to calculate a z score is (test score) + ((mean – test score)/standard deviation). This calculation creates a distribution curve of the values of x, which shows how far away from the mean a particular value is.

The z score is a useful tool for finding outliers or unusual values in a data set. For example, a college admissions officer might compare the SAT scores and GPAs of a prospective student. The applicant’s SAT score and GPA would have different z scores, but the overall pattern would be similar. This would be an indication that the applicant was above average in both areas.