To begin with, let us understand what is machine learning?

**Machine learning (ML) **is the study of computer algorithms that improve automatically through experience. M**achine learning** is the process of teaching a computer system on how to make accurate predictions when fed with data.

Machine learning is used in internet…

There are many different types of tests in statistics like **z-test**, **t-test**, **student t-test**, **chi-square test**, **ANOVA test**, etc. The choice of which test to use totally depends on the type of data, its distribution whether it is normal or not and so on, etc.

Let us now see about…

There are 2 types of errors in hypothesis testing:

- Type I Error
- Type II Error

**Type I error:**

A type I error is also known as a false positive and occurs when a true null hypothesis is rejected incorrectly. The probability of making a type I error is represented by…

**Significance Level:**

The probability with which we will reject a null hypothesis when it is true is the level of significance. It is denoted α. In standard terms, we use 1%, 5% and 10% significance level. …

**Let’s first understand what hypothesis is?**

A **hypothesis** is a specific and testable statement of prediction about the population that might be true.

It is a tentative solution to the problem and then research has to be done to come to a conclusion whether to accept or reject that hypothesis…

In statistics, an **estimate** is an approximation value that is used for some purpose even if input data is incomplete, uncertain, or unstable. An **estimate** is the numeric value of the estimator.

An **estimator** is a rule or formula or function that tells how to calculate an estimate.

In statistics…

I will jot down various definitions of degree of freedom for a better understanding.

In statistics, the **degrees of freedom** indicates the number of independent values that can vary in an analysis without breaking any constraints or rules. Degrees of freedom is commonly abbreviated as df.

The **degree of freedom**…

The **Central Limit Theorem (CLT)** is a statistical theory that states that given a sufficiently large sample size from a population with a finite level of variance, the mean of all samples from the same population will be approximately equal to the mean of the population. …

**Inferential statistics** make inferences, predictions or generalizations about a population based on a sample of data which is taken from the population.

The goal of the **inferential statistics** is to draw conclusions from a sample taken from the population and generalize them to the population.

With inferential statistics, you take…