**Introduction**

Statistics is the science of acquiring, organizing, classifying, analyzing, interpreting, and presenting numerical data to make predictions about the populations from where the sample is drawn. Scientists develop many statistical methods or techniques to understand the data that they collect. They can be either descriptive or **inferential statistics**. With the help of **inferential statistics**, scientists can generalize or make predictions for a population from a specific sample chosen from them. In statistics, population means the entire set of observations that you can make. Studying an entire population is not practically possible for scientists. Therefore, they make samples or subsamples from the population. In this blog, we find out **what is inferential statistics**.

**What is inferential statistics?**

**Inferential statistics** take the help of various models that help you to compare your sample data with other sample data or with any other research.

Therefore, **inferential statistics meaning** can be derived as making inferences about a population based on the samples.

Some of the **inferential statistics examples** can be stated as follows:

- You can randomly select a sample of marks received by the students in the 12th-grade board exam.
- You can stand in a mall and ask a sample of 100 people whether they like shopping at a particular store.
- You can survey whether a certain medicine is effective on a sample of people.

Importance of random sampling in inferential statistics –

While collecting a sample from a population, there must be a systematic way of selecting them. From the random **sampling** method, all the individual items from the population have an equal chance of getting selected in the samples. Therefore, the samples selected are free from unwanted biases. This method requires careful planning from the beginning.

**Inferential statistics and Descriptive statistics**

A **descriptive statistic** is like a summary statistic that describes or summarizes the characteristics of the data. In other words, descriptive statistics describe the data, whereas **inferential statistics** allows you to make predictions about the data. The different tools used in descriptive data include the sample mean, sample standard deviation, bar chart, boxplot, the shape of the sample probability distribution, etc. The distribution concerns the frequency of each value, and the central tendency concerns the mean of values. The variability concerns how spread out the values are.

Some of the differences between **descriptive and inferential statistics **are given below;

- Descriptive statistics is concerned with describing the population of the sample. Inferential statistics are used to conclude about the population after thorough observation and analysis.
- Descriptive statistics collects, analyzes, organizes, and presents the data in a meaningful way. Inferential statistics estimate the parameters of the data, test hypothesis, and predicts future outcomes.
- Descriptive statistics are used when the dataset is small, whereas inferential statistics are used when large.
- In descriptive statistics, the final result is displayed in a diagrammatic or tabular form. On the other hand, the final result in inferential statistics is displayed in the form of probability.
- The tools used in descriptive statistics are measures of dispersion and measures of central tendency. The tools used in Inferential statistics are analysis of variance and hypothesis test.

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**Importance of Inferential statistics**

The importance of inferential statistics are defined as follows:

- To make conclusions about the population from its sample.
- To conclude whether the sample selected is statistically significant to the whole population.
- To compare two models to find out which one is more statistically significant than the other.
- In the case of feature selection, whether the addition or removal of a variable will improve the model or not.

**Inferential statistics and its types**

There are mainly two **types of** **inferential statistics, **which are also known as inferential statistics methods**,** are described below:

- Estimation of parameters – This is the area where a statistic from your sample data is taken, and that data is used to define something about the population parameter. They can also be known as confidence intervals.
- Testing hypotheses – This is the area where you can use the sample data to answer the research questions, which may include – ” Are the means of two or more populations different from each other?”. These hypothesis tests mainly allow concluding for the entire population. However, there might be some errors in hypothesis testings, namely, Type 1 error and Type 2 error.

The steps of hypothesis testing are described below:

- Step 1 – Mention the null and alternative hypotheses.
- Step 2 – Selecting the appropriate inferential statistical test.
- Step 3 – Select the level of significance.
- Step 4 – Performing the test.
- Step 5 – Make a conclusive statement that can be derived from the result of the test.

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**Conclusion**

**Inferential statistics** is a powerful tool that must be used properly to conclude about a population. It helps the scientists to analyze and interpret the data. Any wrong application or interpretation of **inferential statistics** may lead to distortion of final results. Hence, you must carefully apply tools or methods of inferential statistics to achieve the most accurate results. The Jigsaw Academy offers **data science certification** courses that can help you gain your **data science certificate.**

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