Inferential vs. Descriptive Statistics

Statistics refers to collecting, analyzing, and interpreting data using the appropriate mathematical tools. Statistics is an essential tool that researchers in all fields use. Whether an undergraduate student struggling with advanced statistics problems or a professional statistician analyzing computer data, a basic understanding of descriptive statistics vs. inferential statistics can help your results be more precise and insightful.

What is Inferential Statistics?

Inferential statistics is drawing inferences from a set of observed data. Inferential statistics aims to generalize from the sample, or small unit, to a larger population. When conducting an assumption, we are interested in how the outcome of our study differs between groups. In other words, we want to know if our results would be different if we conducted the same experiment or survey with a diverse population.

Types of Inferential Statistics

Hypothesis tests, regression analysis, and confidence intervals are the main inferential statistical techniques used in research. They are different ways of testing whether a statistical relationship exists between two variables.

A hypothesis test is a way of testing whether one variable is related to another variable. For example, you may be interested in seeing if there is a relationship between age and income (a variable that can take values between 0 and 100). The null hypothesis is that there is no relationship between these two variables. However, if your data shows some connection, the alternative view is that there is a relationship.

Regression analysis tests whether a given variable affects another variable. This can be done by taking the square root of the sum of squared residuals and comparing it to zero (to determine if there is linearity). With linearity, it's possible to predict what will happen with this data set.

Confidence intervals are one of the essential types of inferential statistics. They are used to determine whether or not a sample mean statistically different from some reference value. The confidence interval tells us how large we can expect that difference to be, and it also tells us how accurate our estimate of the population parameter is.

What is Descriptive Statistics?

Descriptive statistics is the study of descriptive data, usually collected from observations made on a sample. Descriptive statistics include:

• Measures of central tendency (such as the mean).
• Measures of variability (such as the standard deviation).
• Information about how much each observation differs from the norm.

Descriptive statistics can be used to describe the characteristics of an entire population, or they can be used to describe specific groups in a population. Descriptive statistics are often used in conjunction with inferential statistics, which are used to make valid conclusions about large populations.

Types of Descriptive Statistics

Descriptive statistics are used to describe the values of a particular variable along with its frequency. For example, you can use descriptive statistics to describe the height and weight of a group of people.

Central tendency measures how a group of data relates to the overall population. It is often used to determine where data fits within a distribution. The central tendency is usually determined by taking the average or median value of the data set.

Frequency distribution measures how often each value occurs in the given population. Frequency distributions can determine how many people have a particular characteristic (e.g., age).

The variability or dispersion of a variable is the measure of how widely it falls around a mean. Variability is measured in terms of standard deviation (SD).

The measure of variability is called the standard deviation. It is calculated from the mean and standard error. The standard deviation measures how much the values in a group are spread out from the mean. The larger the standard deviation, the greater the dispersion of points around their mean value.

A standard deviation is essential for determining whether a sample is representative of a population. For example, suppose you know that your data set has considerable variability and outliers. In that case, your sample may need to be more representative of the population as a whole. In this case, it would be best to look at another data set with similar characteristics to yours and see if they have much more variability than what you have found in yours.

Inferential vs. Descriptive Statistics

The difference between inferential and descriptive statistics is that inferential statistics are done to predict future data. In contrast, descriptive statistics are done to describe past data.

Inferential statistics are used to make predictions about future data based on historical data. For example, if a company has been in business for 10 years, it can predict its sales in the next year by using past sales as a reference point. This is an example of inferential statistics.

Descriptive statistics are used to describe past data and provide insight into how that past data compares to other similar situations. For example, suppose a company's sales have increased yearly for 10 years. In that case, their sales will likely continue to grow each year for another 10 years before finally plateauing at some point in time (which is also very likely). This is an example of descriptive statistics.

We have covered the fundamentals of inferential and descriptive statistics in this article. They both involve making assumptions about a population based on sample data collected. Inferential statistics are used for this purpose and are often conducted to make decisions about future data based on previous data that is already known. Descriptive statistics are for sharing your data with others.