Types of Descriptive Statistics in AI

Descriptive statistical analysis helps you to understand your data and is a very important part of machine learning. This is due to machine learning being all about making predictions. On the other hand, statistics is all about drawing conclusions from data, which is a necessary initial step. In this post, you will learn about the most important descriptive statistical concepts. They will help you understand better what your data is trying to tell you, which will result in an overall better machine learning model and understanding.

Descriptive Statistics describes the characteristics of a data set. It is a simple technique to describe, show and summarize data in a meaningful way. You simply choose a group you’re interested in, record data about the group, and then use summary statistics and graphs to describe the group properties. There is no uncertainty involved because you’re just describing the people or items that you actually measure. You’re not aiming to infer properties about a large data set.

Descriptive statistics involves taking a potentially sizeable number of data points in the sample data and reducing them to certain meaningful summary values and graphs. The process allows you to obtain insights and visualize the data rather than simply pouring through sets of raw numbers. With descriptive statistics, you can describe both an entire population and an individual sample.

Join one of the hottest industries today and upgrade your salary with Simplilearn’s Caltech Post Graduate Program In Data Science. Enroll today!

How are Descriptive Statistics used?

These statistics can be univariate (one variable) or bi/multivariate (two or multiple variables). With univariate analysis, the distribution of a single variable is most often studied, in particular:

  • Central tendency – Such as mean, median, and mode
  • Dispersion – The range, quartiles or other groupings of the dataset
  • Measures of spread – Such as variance and standard deviation

With bi or multivariate analysis, the relationships between the variables are added to the analysis. Some of the most important statistics for study include:

  • Correlation – Examples include Pearson’s r if both variables are continuous, or Spearman’s rho if one or both are not continuous
  • Covariance – The scale that variables are measured on
  • Linear slope – Useful in regression analysis to reflect the relationship between variables.

What Is Inferential Statistics?

In Inferential Statistics, the focus is on making predictions about a large group of data based on a representative sample of the population. A random sample of data is considered from a population to describe and make inferences about the population. This technique allows you to work with a small sample rather than the whole population. Since inferential statistics make predictions rather than stating facts, the results are often in the form of probability.

The accuracy of inferential statistics depends largely on the accuracy of sample data and how it represents the larger population. This can be effectively done by obtaining a random sample. Results that are based on non-random samples are usually discarded. Random sampling – though not very straightforward always – is extremely important for carrying out inferential techniques.

Types of Descriptive Statistics

There are three major types of Descriptive Statistics.

1. Frequency Distribution

Frequency distribution is used to show how often a response is given for quantitative as well as qualitative data. It shows the count, percent, or frequency of different outcomes occurring in a given data set. Frequency distribution is usually represented in a table or graph. Bar charts, histograms, pie charts, and line charts are commonly used to present frequency distribution. Each entry in the graph or table is accompanied by how many times the value occurs in a specific interval, range, or group.

These tables of graphs are a structured way to depict a summary of grouped data classified on the basis of mutually exclusive classes and the frequency of occurrence in each respective class.

2. Central Tendency

Central tendency includes the descriptive summary of a dataset using a single value that reflects the center of the data distribution. It locates the distribution by various points and is used to show average or most commonly indicated responses in a data set. Measures of central tendency or measures of central location include the mean, median, and mode. Mean refers to the average or most common value in a data set, while the median is the middle score for the data set in increasing order, and mode is the most frequent value.

3. Variability or Dispersion

A measure of variability identifies the range, variance, and standard deviation of scores in a sample. This measure denotes the range and width of distribution values in a data set and determines how to spread apart the data points are from the center.

The range shows the degree of dispersion or the difference between the highest and lowest values within the data set. The variance refers to the degree of the spread and is measured as an average of the squared deviations. The standard deviation determines the difference between the observed score in the data set and the mean value. This descriptive statistic is useful when you want to show how to spread out your data is and how it affects the mean.

Descriptive Statistics is also used to determine measures of position, which describes how a score ranks in relation to another. This statistic is used to compare scores to a normalized score like determining percentile ranks and quartile ranks.

Learn from experts at one the best Universities in the US! Become a Master in data science with Simplilearn’s Data Science Bootcamp! Enroll today!

Types of Inferential Statistics

Inferential Statistics helps to draw conclusions and make predictions based on a data set. It is done using several techniques, methods, and types of calculations. Some of the most important types of inferential statistics calculations are:

1. Regression Analysis

Regression models show the relationship between a set of independent variables and a dependent variable. This statistical method lets you predict the value of the dependent variable based on different values of the independent variables. Hypothesis tests are incorporated to determine whether the relationships observed in sample data actually exist in the data set.

2. Hypothesis Tests

Hypothesis testing is used to compare entire populations or assess relationships between variables using samples. Hypotheses or predictions are tested using statistical tests so as to draw valid inferences.

3. Confidence Intervals

The main goal of inferential statistics is to estimate population parameters, which are mostly unknown or unknowable values. A confidence interval observes the variability in a statistic to draw an interval estimate for a parameter. Confidence intervals take uncertainty and sampling error into account to create a range of values within which the actual population value is estimated to fall.

Each confidence interval is associated with a confidence level that indicates the probability in the percentage of the interval to contain the parameter estimate if you repeat the study.

Difference Between Descriptive and Inferential statistics

As you can see, Descriptive statistics summarize the features or characteristics of a data set, while Inferential statistics enables the user to test a hypothesis to check if the data is generalizable to the wider population. Now, how can we go from descriptive to inferential statistics? The difference lies in finding the answer to “What is?” vs. “What else it might be?”.

The differences between descriptive statistics vs inferential statistics lie as much in the process as in the statistics reported. Given below are the key points of difference in descriptive vs inferential statistics.

  • Descriptive Statistics gives information about raw data regarding its description or features. Inferential statistics, on the other hand, draw inferences about the population by using data extracted from the population.
  • We use descriptive statistics to describe a situation, while we use inferential statistics to explain the probability of the occurrence of an event.
  • Descriptive statistics, it helps to organize, analyze and present data in a meaningful manner. Inferential statistics helps to compare data and make hypotheses and predictions.
  • Descriptive statistics explains already-known data related to a particular sample or population of a small size. Inferential statistics, however, aims to draw inferences or conclusions about a whole population.
  • We use charts, graphs, and tables to represent descriptive statistics, while we use probability methods for inferential statistics.
  • It is simpler to perform a study using descriptive statistics rather than inferential statistics, where you need to establish a relationship between variables in an entire population.

Become a Data Scientist by learning from the best with Simplilearn’s Caltech Post Graduate Program In Data Science. Enroll Now!

Want to know more about descriptive vs inferential statistics? Follow our Descriptive statistics guide to learn everything about how to compute summary statistics. If you want to make a career in the emerging data science field, it’s very important to pursue a leading Caltech Post Graduate Program In Data Science to learn the basics of statistics and the difference between descriptive statistics vs inferential statistics. Get in touch with us at Simplilearn to help you jump-start a data science career.