📊📈🔍 Hey guys, let’s talk about variance and standard deviation! 📊📈🔍

The **variance** measures how far data values lie from the mean of a distribution. It tells us how spread out the values are from the average. It’s like the difference between a group of people standing close together versus spread out across a wide area. The variance is the average squared deviation of each value from the mean. We square the deviations so that negative and positive deviations don’t cancel each other out. The higher the variance, the more spread out the values are.

The** standard deviation** is the square root of the variance. It tells us the average distance between the mean and the individual data values. It’s like looking at how far each person is from the center of a group.

The variance is the average of the squared deviations. We square the deviations so that negative and positive deviations don’t cancel each other out. The variance is like looking at the average of how far each person is from the center of a group.

The standard deviation is on the same scale as the original data, which makes it easier to interpret. For example, if the data is in inches, the standard deviation will also be in inches. The variance, on the other hand, is on a squared scale, which can be harder to interpret.

The variance and standard deviation are based on squared deviations, which means they give more weight to extreme values or outliers. This can cause the variance and standard deviation to be sensitive to outliers and may not accurately represent the variability of the data.

💡 And there you have it! Variance and standard deviation are useful measures of variability in a distribution, but we have to be careful with outliers and interpretation. 💡