#### Franco Brutti

How many times have you done a data analysis to make more **specific plans for your company?** Surely on several occasions, it's time for you to understand how inferential statistics work so that you can get the most out of them.

A population offers a lot of information about different phenomena or behaviors that are **relevant for making ****business decisions****.** The results of these investigations can make the difference between the success or failure of any company.

Analyzing the **consumption habits of an economic sector** is transcendental for the future of your commercial project. Do you dare to detect it now?

Get comfortable and find out.

**What is inferential statistics?**

Let's start by defining **what inferential statistics is**. It’s the branch of statistics that studies in depth the behavior of a series of variables and the consequences they bring, extending them to broader groups.

It’s very interesting because it gives you the possibility of **creating different routes of action** for different scenarios. For this reason, it’s one of the most important fields of statistics today.

We can say that it’s a deductive branch in which **you can draw conclusions **about a data analysis and this analysis is related to a sample that aims to extend the information to a whole population.

This field also refers to **the study of the frequency with which a certain phenomenon **occurs in the research. In this way, with the sample we can accumulate information from a smaller sector about the object of research.

**What are inferential statistics for?**

The main objective of inferential statistics is to **make it easier to study data **from a smaller scale and then interpret them and attribute conclusions to a larger scale of data.

In other words, we have the possibility of **analyzing large amounts of information** from the observation of a minimum part of them.

Likewise, you can run forecasts of future events that facilitate early action routes in case there are inconveniences.

**Elements of inferential statistics**

Inferential statistics is composed of different elements that are part of the calculation of probabilities. These are:

**1. Elementary Probability**

Elementary probability is the study of possible outcomes of an act and the **frequency it has during its application** on different occasions. The formula used is that of the number of times** a result can occur among the total number of possible cases.**

**2. Permutations**

Secondly, we have the permutations, which are the ways in which **some specific data can be organized. **In this sense, it’s striking that the formula of the factorial of the elements to be organized is used.

**3. Variations**

On the other hand, variations are a **type of calculation** that resemble permutations and that only consider some elements, not all, so the formula to calculate is: n!: (n-r)! In this case, n is the number of data and r is the way in which they can be grouped.

**4. Combinations**

Combinations are the number of ways in which very **specific data can be organized.**

**Why are inferential statistics so important today?**

Inferential statistics when applied in research is one of the most important stages of any research and its relevance lies in the fact that it brings **different elements to the field of study** of different areas in which it’s impossible to cover all the data.

Therefore, in many scenarios, no impact study could be conducted without the implementation of **inferential analysis** because there would be no clear objective for the research.

In other words, inferential statistics is where theory and practice come together.

**4 Objectives of inferential statistics**

Inferential statistics have several objectives. Some of them are:

**1. Foreseeing future events**

First of all, we have the fact of **foreseeing future events.** When we are in the middle of an investigation, we want to know what could happen in the following weeks in order to take action for what is to come.

Well, from the samples we obtain at that moment we can** project different behaviors** that will help us make the adjustments we deem necessary in our business.

**2. Using sample analysis to draw conclusions**

Closely related to the previous point. With inferential statistics, we have the possibility of obtaining samples that help us to draw conclusions according to the behaviors of **a certain sector of the population. **

Forget about working blindly. With this type of statistics, you will have everything under your control.

**3. Studying large blocks of information**

On the other hand, we cannot leave aside the advantage of studying **large amounts of information **from only a small block of information. Nowadays we need to optimize as much time as possible and with this branch of statistics, we will be able to cover a lot with very few resources.

**4. Executing various actions**

Once we have the information we need we can execute the actions we **consider necessary** to achieve the objectives we set out to achieve.

**Characteristics of inferential statistics**

The following are some of the characteristics of inferential statistics:

**1. It’s the deductive stage of an investigation**

In other words, it’s the moment when we **begin to draw conclusions** about a specific investigation. First, all the resources we have in hand are used and once we have them we will be very close to achieving the objectives we set out.

**2. Arithmetic calculations are used**

As you saw in the previous paragraphs, **the above calculations **are present in almost all formulas for sample analysis. At the end of the day, the numbers are never wrong and help to show what is happening at that moment.

**3. Hypotheses are defined in the framework of the research**

When we are in the research process we have the possibility of **defining hypotheses** to be later tested through different formulas.

This is a way of reducing the margin of error and gives you certainty for the future.

**Uses of inferential statistics**

One of the reasons why we love inferential statistics is because they are used in a **large number of fields** that allow us to make projections about what will happen in the future. In this way, we can make more accurate decisions based on data.

Let's take a look at some examples of its current uses:

**1. Scientific research**

First of all, we have scientific research. This branch of statistics is very important to use different techniques such as **analysis of variance and hypothesis testing **to determine that the results of a sample are relevant to a given population.

**2. Business**

On the other hand, companies more frequently use inferential statistics to make relevant choices such as the size of a sample that will determine the **size of product demand**, consumer behavior, and even evaluate the performance of employees.

So, if you want to meet your customer's needs, it's time to take a look at this trend.

**3. Healthcare**

The healthcare industry is a concrete example of how important it is to **establish effective strategies** to optimize resources.

In this sense, inferential statistics are indispensable in medical research to evaluate the **efficiency of some treatments**, as well as new drugs that are coming to the market.

It’s also used to evaluate the risk of some of today's chronic diseases.

**4. Policy**

Politics also rely on statistics to **make projections** about a particular election, and population trends and to conduct surveys to obtain a public opinion about a candidate.

**Types of inferential statistics**

Inferential statistics are currently divided into two categories. Take out paper and pencil and find out what they are:

**1. Hypothesis testing**

The first is hypothesis testing, which allows us to **draw generalizations** about a population based on sample data.

In this sense, it is essential to create both a **null and alternative hypothesis** to then run a statistical test of significance.

Now, a hypothesis test can have either left-tailed, right-tailed, or two-tailed distributions. Therefore, the statistical value will be used to reach a conclusion.

**2. Regression analysis**

Secondly, we have regression analysis, which is performed to calculate **how one variable may change** in relation to another. Different models can be used for this, such as linear, multiple linear, simple linear, nominal, logistic, and ordinal regression.

At this point, it should be noted that linear regression is the** type of regression most used** in inferential statistics. At present, it has different equations that are fundamental to obtain the results we need.

**Examples of inferential statistics**

There are several examples that demonstrate how inferential statistics is currently applied.

Let's see. If you have a company and you want to know if **your customers are happy with the product **you have just launched to the market, you can rely on this branch of statistics to obtain a random sample of consumers to rate the product on a specific scale.

Once you have the data, you can generalize over the entire population that has bought the product to draw conclusions that will help **you decide on the future of the project. **

You can also use a **confidence interval **to determine what the probability is that the average rating of the population will reach that interval.

In this way, inferential statistics can help you make precise decisions on how to improve the quality of your products or promote them more efficiently.

**Differences between descriptive and inferential statistics**

It’s true that both types of statistics are present today and are of great importance for **different economic sectors.** However, they have differences that are important to take into account.

The first is that descriptive statistics are used to summarize and **describe all the data in a sample**, while inferential statistics are used to make much more precise generalizations from a small sample.

On the other hand, descriptive statistics focus on describing the characteristics of a sample such as **mode, median, and mean.** These parameters provide a simple understanding of the data and can be used to summarize findings from the sample.

Inferential statistics, on the other hand, is used exclusively to make **different predictions** make decisions based on data from a sample drawn from the population, just as you read it.

For this purpose, it relies on techniques as diverse as confidence intervals, hypotheses, and regression analysis to obtain the sample you need so much.

There’s no doubt that inferential statistics is an excellent way to **put data to work for us** from a series of tools that will allow us to make more accurate choices.

Although it can be somewhat intimidating, the truth is that it’s much simpler than it seems. Come on, don't let **the numbers scare you, **and start working to offer customized products to your customers.

We hope that with this information you have the foundation you need to meet the goals you set for your business. Are you ready to take the next step?