# Inferential Statistics Examples: A Brief Explanation (Read this!)

Understanding inferential statistics with the examples is the easiest way to learn it. It is one branch of statistics that is very useful in the world of research. It has a big role and of the important aspect of research.

Today, inferential statistics are known to be getting closer to many circles. Not only by students or academics, but the use of these statistics is also often used by survey institutions in releasing their results.

This proves that inferential statistics actually have an important role in our lives.

## What is Inferential Statistic?

If you see based on the language, inferential means can be concluded.

In general, inferential statistics are a type of statistics that focus on processing sample data so that they can make decisions or conclusions on the population.

Inferential statistics focus on analyzing sample data to infer the population.

The flow of using inferential statistics is the sampling method, data analysis, and decision making for the entire population.

Inferential statistics are used by many people (especially scientist and researcher) because they are able to produce accurate estimates at a relatively affordable cost.

## Advantages of Using Inferential Statistics

Inferential statistics have different benefits and advantages.

1. A precise tool for estimating population

The main purpose of using inferential statistics is to estimate population values. With the use of this method, of course, we expect accurate and precise measurement results and are able to describe the actual conditions.

2. Highly structured analytical methods

Inferential statistics have a very neat formula and structure. The method used is tested mathematically and can be regarded as an unbiased estimator.

## Inferential Statistics Examples

There are lots of examples of applications and the application of inferential statistics in life. However, in general, the inferential statistics that are often used are:

### 1. Regression Analysis

Regression analysis is one of the most popular analysis tools. Regression analysis is used to predict the relationship between independent variables and the dependent variable.

Using this analysis, we can determine which variables have a significant effect in a study.

For example, you want to know what factors can influence the decline in poverty. You use variables such as road length, economic growth, electrification ratio, number of teachers, number of medical personnel, etc.

After analysis, you will find which variables have an influence in reducing the poverty rate.

### 2. Hypothesis test

Hypothesis testing is a statistical test where we want to know the truth of an assumption or opinion that is common in society. Usually, this test is used to find out about the truth of a claim circulating in the community.

Hypothesis testing also helps us to prove whether the opinions or things we believe are true or false.

For example, we often hear the assumption that female students tend to have higher mathematical values ​​than men. Is that right?

To prove this, you can take a representative sample and analyze the mathematical values ​​of the samples taken.

By using a hypothesis test, you can draw conclusions about the actual conditions.

Can you use the entire data on the overall mathematics value of students and analyze the data? Certainly very allowed.

But, of course, you will need a longer time in reaching conclusions because the data collection process also requires substantial time.

### 3. Confidence Interval

Confidence interval or confidence level is a statistical test used to estimate the population by using samples. With this level of trust, we can estimate with a greater probability what the actual population value is.

When using confidence intervals, we will find the upper and lower limits of a statistical test that we believe there is a population value we estimate.

When we use 95 percent confidence intervals, it means we believe that the test statistics we use are within the range of values ​​we have obtained based on the formula.

For example, we want to estimate what the average expenditure is for everyone in city X. Therefore, research is conducted by taking a number of samples. The results of this study certainly vary.

Therefore, we must determine the estimated range of the actual expenditure of each person. The hope is, of course, the actual average value will fall in the range of values ​​that we have calculated before.

At the last part of this article, I will show you how confidence interval works as inferential statistics examples.

### 4. Time series analysis

As you know, one type of data based on time is time series data. Sometimes, often a data occurs repeatedly or has special and common patterns so it is very interesting to study more deeply.

Time series analysis is one type of statistical analysis that tries to predict an event in the future based on pre-existing data. With this method, we can estimate how predictions a value or event that appears in the future.

Example: every year, policymakers always estimate economic growth, both quarterly and yearly. By using time series analysis, we can use data from 20 to 30 years to estimate how economic growth will be in the future.

Procedure for using inferential statistics

1. Determine the population data that we want to examine

2. Determine the number of samples that are representative of the population

3. Select an analysis that matches the purpose and type of data we have

4. Make conclusions on the results of the analysis

## Differences in Inferential Statistics and Descriptive Statistics

Inferential statistics and descriptive statistics have very basic differences in the analysis process. In general, these two types of statistics also have different objectives.

1. Descriptive statistics aim to describe the characteristics of the data. While statistical inferencing aims to draw conclusions for the population by analyzing the sample.

2. Descriptive statistics are usually only presented in the form of tables and graphs. The test statistics used are fairly simple, such as averages, variances, etc. While inferential statistics, the statistics used are classified as very complicated. Not everyone is able to use inferential statistics so special seriousness and learning are needed before using it.

Therefore, we cannot use any analytical tools available in descriptive analysis to infer the overall data.

## How to make inferential statistics as a stronger tool?

Probably, the analyst knows several things that can influence inferential statistics in order to produce accurate estimates. The main key is good sampling.

Samples taken must be random or random. That is, there should not be certain trends in taking who, what, and how the condition of the sample.

The selected sample must also meet the minimum sample requirements. Actually, there is no specific requirement for the number of samples that must be used to be able to represent the population. However, many experts agree that the number of samples used must be at least 30 units.

Samples must also be able to meet certain distributions. Usually, the commonly used sample distribution is a normal distribution. Although sometimes, there are cases where other distributions are indeed more suitable.

Make sure the above three conditions are met so that your analysis results don’t disappoint later.

## Case Study of Inferential Statistics

There are several types of inferential statistics examples that you can use. But in this case, I will just give an example using statistical confidence intervals.

Suppose a regional head claims that the poverty rate in his area is very low. To prove this, he conducted a household income and expenditure survey that was theoretically able to produce poverty.

Considering the survey period and budget, 10,000 household samples were selected from a total of 100,000 households in the district.

Based on the survey results, it was found that there were still 5,000 poor people. Of course, this number is not entirely true considering the survey always has errors.

Therefore, confidence intervals were made to strengthen the results of this survey.

Based on the results of calculations, with a confidence level of 95 percent and the standard deviation is 500, it can be concluded that the number of poor people in the city ranges from 4,990 to 5010 people.

Closing

Inferential statistics examples have no limit. They are available to facilitate us in estimating populations. Its use is indeed more challenging, but the efficiency that is presented greatly helps us in various surveys or research.

Descriptive statistics and inferential statistics are data processing tools that complement each other. It makes our analysis become powerful and meaningful.

### 2 thoughts on “Inferential Statistics Examples: A Brief Explanation (Read this!)”

1. Really wonderful explanation