Six Sigma is a data-driven approach for problem-solving. Six Sigma involves many mathematical and statistical concepts, such as the normal distribution curve. Lean Six Sigma courses cover the most important statistical concepts needed to solve problems according 6 sigma rules. Six Sigma principles are heavily based on understanding the normal distribution curve, as briefly explained in the free online Six Sigma courses. Let’s take a look at the normal distribution curve.

What is the normal distribution curve of data?

The mathematical concept of normal distribution, also known as Gaussian distribution, is sometimes called “Normal Distribution Curve” (or “Bell Curve”) and used to describe it. It is the shape created when a line is drawn using data points that correspond to the criteria of “Normal Distribution”.

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Natural phenomena follow a normal distribution curve

Many natural phenomena exhibit a pattern known as the ‘Normal Distribution Curve or ‘Bell Curve. The results of measuring the height of women around the world will look like a bell if you take the measurements. This pattern also applies to temperature. If you measured the average July day temperature in the US each year, the bell curve pattern would be apparent. You can also measure the heights of your coworkers at work or how long they take to brew a cup of tea. This will give you an approximate normal distribution curve.

The structure of a normal distributions curve

Let’s take a look at the structure and function of a normal distribution curve. The center is the point at which the most data points are found. It would also be the highest point of the arc. This point is also known as the average or mean of the normal distribution curve. Let’s take the example of height. There will be more women of average height than other heights, so the normal distribution curve will have the highest value for women worldwide.

The normal distribution curve’s mean, median, or mode.

In the case of normally distributed data, both the median (or the mode) will equal the mean. Let’s look at the difference between mode, median, and mean. The mean is the sum total of all data points divided by the number. The median is the average value if you add all the values, from the smallest to the largest. The mode, on the other hand, is the number that appears the most often in a collection of data points. Normal distributions will have the mean, median, and mode all being the same.

Outliers in a normal distribution

The normal distribution curve has a concentrated center and decreases on both sides. This is significant because the data tends to have fewer instances of unusually high values (outliers or special causes for variation (SCV) than other distributions. The curve flattens because there are few extreme numbers at either the lower or higher ends of the scale. This is what gives normal distribution curve its bell-like appearance.

Normal distributions in terms standard deviations and means

Let’s take a look at the normal distribution curve from a different angle. A curve graph is dependent on two factors: the mean and the standard deviation. Let’s review briefly the definition of standard deviation. The standard deviation measures how closely grouped or widely spaced data appear. It is one measure of dispersion. A standard normal distribution has an average of zero and a standard error of one. The center is identified by the mean, while the standard deviation determines height and width.

A large standard deviation, for example, creates a flat and wide-shaped bell. However, a small deviation can result in a smaller bell.