What Companies Should Know About Descriptive Statistics
CIOReview
CIOREVIEW >> Big Data >>

What Companies Should Know About Descriptive Statistics

By CIOReview | Thursday, July 8, 2021

Descriptive analytics forms the foundation of quantitative analysis of any set of data as it gives an understanding of the nature of the data collected.  

FREMONT, CA: Quantitative data analysis of an extensive collection of data is feasible due to numerical computations that provide insight into the nature of the data and make it easier to recognize trends. The two methodologies utilized for this are descriptive statistics and inferential statistics.

Descriptive statistics is a type of statistics that describes or highlights the data's essential traits or qualities. It assigns numerical values to the collected samples to indicate their trend. It turns vast amounts of data into a more straightforward, comprehensible format that is easier to comprehend and interpret. Graphs and tables accompany it, and descriptive statistics provide a comprehensive summary of the entire data set.

Descriptive statistics show that analysis is the primary goal, but inferential statistics use descriptive values for making future predictions for a more comprehensive set of data. As a result, descriptive statistics are the first and most crucial stage in quantitative data analysis.

TYPES OF DESCRIPTIVE STATISTICS

Four different forms of descriptive statistics can be used to assess a set of data attributes.

Measures of Frequency

This measure determines how frequently a particular variable appears in the distribution. It displays how often a response or variable occurs and can be evaluated in numbers or percentages.

Measures of Central Tendency

Measures of central tendency indicate the average or most common variable in the data collection. They calculate the mean, median, and mode to locate specific points.

Measures of Variation or Dispersion

This illustrates how evenly distributed the responses in the data set are. It aids in determining the distance between the greatest and lowest values and how far individual values differ from the mean or average. The range, standard deviation, and variance are used to construct variation measures.

Measure of Position

This measure assesses how individuals' values relate to one another. A standardized value is used in this method of calculation. The measures of position are percentiles and quartile ranks.

IMPORTANCE OF DESCRIPTIVE STATISTICS

Any quantitative data analysis procedure starts with descriptive statistics. It's the first stage in describing the data and its characteristics. It provides a simplified image of the data set, regardless of its size or complexity, and facilitates interpretation. The principles of descriptive statistics are crucial for any innovative statistical analysis since the measures and values gained through descriptive statistics are necessary.