# Understanding Type I And Type II Errors

Starting on a lighter note, let’s dig into one area called hypothesis testing. In statistics, hypothesis testing is used to decide whether a particular claim made on a population of data is true or false from a sample available. Just like the image above, a man cannot be pregnant (biologically), which is a claim made on pregnancy in humans. In hypothesis testing, this factual statement forms the ‘null hypothesis’ where it is tested for validity (right or wrong).

Generally, the norm in hypothesis testing is to reject the null hypothesis. This is because the null hypothesis is the basis for validating what we are testing. The opposing statement called ‘alternative hypothesis’ contests with the null hypothesis. Altogether, hypothesis testing combines both these arguments to check which is right or wrong in the sample data.

### Type I Error

In formal terms, if a null hypothesis is rejected even when it is true and the alternative hypothesis is accepted, this error is known as a Type I error. With respect to the cover image, ‘man cannot be pregnant’ is the null hypothesis and ‘man is pregnant’ is the alternative hypothesis. Despite this fact, if the doctor rejects the statement, he commits a Type I error. In other words, the man is considered pregnant!

### Type II Error

If the null hypothesis is accepted when it is false and the alternate hypothesis is rejected, this leads to what is known as Type II error. Again, back to the image, ‘woman is not pregnant’ is the null hypothesis and ‘woman is pregnant’ is the alternative hypothesis. Suppose the doctor says the lady is not pregnant even when she is, typically leads to Type II error where the null hypothesis is deemed right and alternative hypothesis is said to be wrong.

### How to Reduce These Errors

In the case of Type I error, a smaller level of significance will generally help. Before beginning with hypothesis testing, this feature is considered if the null hypothesis is assumed to be true. In Type II error, another concept called Power, in addition to the significance level, helps overcome the effect of this error (more about this can be found here).

Overall, before running a statistical experiment, a good deal of right data is suggested for eliminating differences statistically.

I research and cover latest happenings in data science. My fervent interests are in latest technology and humor/comedy (an odd combination!). When I'm not busy reading on these subjects, you'll find me watching movies or playing badminton.

## Our Upcoming Events

### Telegram group

Discover special offers, top stories, upcoming events, and more.

### Discord Server

Stay Connected with a larger ecosystem of data science and ML Professionals

### Apple Launches iCringe with a Sustainability Twist

With Mother Nature in mind, Apple is making impactful strides towards carbon-neutral products. However, there is a slight hiccup

### Data Science Hiring Process at Zoho

Zoho has over 10 open positions for both freshers and experienced professionals.

### Will AGI Be Built in China?

AGIEval Seems to Think So

### NVIDIA Expands Cloud Business with Investments, Partnerships

With NVIDIA partnership, Hugging Face users get access to SOTA GPUs and infrastructure needed to rapidly train and finetune foundation models at scale and drive a new wave of enterprise LLM development.

### Intel Soon to be on Par with NVIDIA

A green CPU with a blue GPU might soon be possible.

### Shell Hackathon to Protect Against Cyber Threats

The aim of the Cyber Threat Detection Hackathon is to build a model capable of identifying code in a body of text.

### ChatGPT is Down, I Can’t Code Anymore

Don’t they know I have a product to ship?

### Decoding SAP Labs’ Generative AI Motto

The German ERP software provider is investing heavily in upskilling its employees.

### Why AI Tech Honchos are Meeting Behind Closed Doors

What transpired when the who’s who of tech leaders convened in Capitol Hill last week to discuss AI behind closed doors?

### AI Clock is Ticking: Wake Up Call for Education Institutions

It’s not too late