Hey everyone!!! Hope you all are doing great. In this article, we shall be covering Inferential statistics and its types. Inferential statistics is a field of statistics that deals with making inferences about a population based on a sample of data. In simple words, they take some samples of the data and draw conclusions from it and assumes the same conclusion for the rest of the data. Inferential statistics plays an important role in machine learning by helping to test hypotheses, estimate population parameters, and make predictions.

There are two main types of inferential statistics: estimation and hypothesis testing.

1. Estimation: Estimation involves using sample data to estimate a population parameter. The most common way to estimate a population parameter is to calculate a confidence interval, which is a range of values that is likely to contain the true population parameter with a certain level of confidence. The level of confidence is typically set at 90%, 95%, or 99%, and the confidence interval is calculated based on the sample size and the standard deviation of the sample.

2. Hypothesis testing: Hypothesis testing involves testing a hypothesis or claim about a population based on sample data. The hypothesis is either accepted or rejected based on the results of the test. Hypothesis testing involves the following steps:

• State the null hypothesis and alternative hypothesis

• Choose a significance level (alpha) for the test

• Calculate the test statistic based on the sample data

• Determine the p-value of the test statistic

• Compare the p-value to the significance level

• Draw a conclusion based on the comparison of the p-value and the significance level

• There are several types of hypothesis tests, including:

  1. One-sample t-test

  2. Two-sample t-test

  3. Paired t-test

  4. Chi-square test

  5. ANOVA (analysis of variance)

Note-: We shall be covering these tests in the next article ( Subscribe to the newsletter for the next article :) )

Inferential statistics is used in machine learning in a variety of ways. For example, it is used to evaluate the performance of models by comparing their predictions to actual data. It is also used to estimate population parameters, such as the mean or standard deviation of a dataset, which can be useful for understanding the characteristics of the data. In addition, hypothesis testing can be used to compare different models or algorithms to determine which is the best fit for a particular problem.

In conclusion, inferential statistics is a crucial tool for machine learning. By using sample data to make inferences about a population, it allows us to test hypotheses, estimate population parameters, and make predictions. Understanding inferential statistics is essential for anyone working in machine learning, as it enables us to evaluate models and make data-driven decisions. Hope you got value out of it . You can subscribe to the newsletter for the article.

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