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Exploring the Concept of Standard Deviation Through Examples


In statistics, it is essential to comprehend standard deviation while analyzing and interpreting data. Relatively widely dispersed information elements are suggested by a higher standard deviation, whereas closely organized information is indicated by a lower standard deviation.

The statistical data that determines the degree of variability or dispersion of the provided data set is used to calculate the standard deviation. It provides specifics regarding the data items and the level of variability surrounding the median or mean.

Standard deviation is widely used in different fields of mathematics or statistics including finance, quality control, and scientific research. It allows us to compare the data set’s reliability depending on the measurements and gives predictions of how the data is dispersed based on the variability of the data.

In this article, we will discuss the basic definition of standard deviation, the steps to find it, its physical interpretations in statistics in detail.

Standard Deviation

Standard deviation expresses us how much the distinct data points stray from the mean. A mathematical value of standard deviation expressions how the integer is nearby to the average of the given data set if the integer is inferior then data points are quicker to the average while a greater value proposes superior spreading or feast of the data points about the average of the data set.

Its mathematical formula is written as.

σ = S.D=  [ (xj - x̄) 2/ (N-1)]


  • σ = standard deviation,
  • xj = “jth” value in the given data,
  • N = total number,
  • x̄ = is known as mean value of the given set.

Steps to Determine Standard Deviation

To compute standard deviation using the data, follow these steps:

Step 1:

The adding together each data point & reducing the result by the whole amount of data points, you can identify the mean (average) of the data collection.

Step 2:

Calculate the variance by deducting the average via every single value in the data. Every data point’s deviation from the mean is shown by this.

Step 3:

This step is important to eliminate negative values and emphasize the magnitude of the deviations.

Step 4:

The dataset then finds the average of the squared deviations by summing them up and dividing by the one a smaller number of data points.

Step 5:

Value take the square root of the variance value that represents in the above steps. This gives an indication of how far beyond the average the data points are dispersed.

Role of Standard Deviation in Statistics

To comprehend the range and volatility among information, the median is frequently employed in statistics, studies, and information analytics. It facilitates the comparison of various data sets, the evaluation of measurement reliability, and the formulation of sound choices in light of data variability.

To measure the amount given spread or variation in a set of data, one uses a statistical metric known as standard deviation. It provides crucial information about how widely distributed the data points are from the average or median.

Reason to Calculate Standard Deviation

Here we can discuss the stages of reason to calculate standard deviation bellow as,

  • The standard deviation allows us to quantify the degree of dispersion or spread within a data set. You are able to evaluate the data’s coherence its variety by analyzing its distribution of data points.
  • Standard deviation enables us to compare different data sets and determine which one has a greater or lesser degree of variability. This comparison helps in concluding the nature and characteristics of the data.
  • Standard deviation is used to assess the reliability or precision of measurements. Higher variance alongside potentially mistaken measurements are indicated by an elevated standard deviation, whereas lower ranges are indicative of more accurate readings.
  • A key element of many statistical analyses, such as regression, trust intervals, as well as hypothesis testing, is the standard deviation. It facilitates the assessment of the findings’ importance and the drawing of conclusions about the population from sample data.

Few interpretations of Standard Deviation

The setting in which the facts is under scrutiny affects how the standard deviation is physically interpreted.

  • In experimental sciences, the standard deviation represents the measurement precision or the degree of uncertainty associated with the measurements. A smaller standard deviation indicates more precise and consistent measurements, while a larger standard deviation suggests greater variability and potential errors in the measurements.
  • The standard deviation is an important component of statistical models. It helps to assess a equation’s accuracy and how well it matches the data. The deviation from the mean of the leftovers in regression analysis—that is, the variation among actual and projected values—offers information on how variable the model’s predictions are.

Example section:

Example 1:

If the statistical data is 3, 4, 11, 12, and 15 then evaluate the standard deviation of the given data set.


Step 1:

Write the above data in the set form carefully from the question.

Dataset = {3, 4, 11, 12, 13}, N = 7, S.D =?

Step 2:

Write the mathematical formula with detailed of the Standard Deviation.

σ = S.D= √ [∑ (xj - x̄) 2/ (N-1)]

Step 3:

Find the mean of the given data using the data from the “step 1”.

x̄ = (∑ xj)/N

x̄ = ∑ (3 + 4 + 11 + 12 +15) /5

x̄ = 45/5

x̄ = 9

Step 4:

Evaluation of the variability involves comparing the inverse of the departures from the mean.

(3 – 9)2 = (-6)2 = 36, (4 – 9)2 = (-5)2 = 25

(11 – 9)2 = (2)2 = 4, (12 – 9)2 = (3)2 = 9

(15 – 9)2 = (6)2 = 36

Step 5:

Now, divide every value by the difference of one to determine the variance. This is done by adding up all of the values mentioned before.

V = Variance = (36 + 25 + 4 + 9 + 36) / (5 – 1)

V = Variance = (110) / 4

V = Variance = 27.5

Step 6:

For “S.D.” take the square root of variance.

S.D = √27.5 = 5.24

S.D = 5.24

S.D = 5.24 is the standard deviation if the dataset is 3, 4, 11, 12, and 15.

Final Words

In this article, we discussed the basic idea of standard deviation with its definition, steps to find it, its role in statistics, and a few interpretations of standard deviation. Moreover, to understand the concept of standard deviation in a better way solved examples.


Question 1:

Standard deviation


A collection of data’s standard deviation indicates how dispersed they are from the mean. It displays the average deviation between each data point and the primary trend.

Question 2:

Why is a standard deviation high or low relevant?


It produces a great deal of inherent variance within the data when the standard deviation is high since it shows the distance that the points of information are from the mean. A low standard deviation denotes a smaller variance because it shows that the data points are concentrated near the mean.

Question 3:

Difference between standard deviation and variance


The standard deviation squared equals variance. Using square millimeters, it represents identical thing as standard deviation. Because it uses the same units as the original data, the standard deviation is frequently chosen.


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