{"id":485,"date":"2021-08-23T11:19:21","date_gmt":"2021-08-23T11:19:21","guid":{"rendered":"https:\/\/www.wivekeys.com\/?p=485"},"modified":"2022-12-05T18:11:27","modified_gmt":"2022-12-05T18:11:27","slug":"central-limit-theorem-important-points","status":"publish","type":"post","link":"https:\/\/www.wivekeys.com\/central-limit-theorem-important-points\/","title":{"rendered":"Central Limit Theorem – Important Points"},"content":{"rendered":"\n

The branch of mathematics which deals with developing and studying methods for analyzing, interpreting, collecting, and presenting data is known as Statistics. This is an important topic for the JEE Main exam. Students are advised to learn important theorems and formulas of statistics so that they can easily solve related problems.<\/p>\n\n\n\n

Students can expect 4 marks questions from statistics. Central limit theorem<\/strong><\/a> (CLT) is an important statistical theory which states that the samples will be normally distributed and the mean of samples will be approximately equal to the mean of the whole population when the large sample size is having finite variance. This theorem states that for any population with mean \u03bc and standard deviation, the distribution of the sample mean for sample size N has to mean \u03bc and standard deviation \u03c3\/\u221an.<\/p>\n\n\n\n

This theory is applied in simplifying analysis with stock index and many more. If the sample size gets bigger, the mean of the sample will come closer to the actual population mean. If the sample is small in size, the actual distribution of the data may or may not be normal. When the sample size gets bigger, it can be approximated by a normal distribution.<\/p>\n\n\n\n

<\/a>Statement<\/h2>\n\n\n\n

This theorem states that whenever a random sample of size n is taken from any distribution with mean and variance, then the sample means will be approximately normally distributed with mean and variance. The higher the value of the sample size, the better the approximation to the normal.<\/p>\n\n\n\n

<\/a>Assumptions Of CLT<\/h3>\n\n\n\n

Following are the assumptions of this theorem.<\/p>\n\n\n\n