Central Limit Theorem: Explained in simple words

The central limit theorem states that the distribution of sample means approximates a normal distribution as the sample size gets larger, regardless of the population's distribution.
The Central Limit Theorem states that the sampling distribution of the sample means approaches a normal distribution as the sample size gets larger: no matter what the shape of the population distribution.
Graphically it looks like a bell shape. If we do one of the simplest types of test that is rolling a fair die. The more times we roll the die, the more likely the shape of the distribution of the means tends to look like a normal distribution graph or bell shape.
For example, the distribution of the heights of all 20-year-old people in a country. It is almost impossible and of course not practical, to collect this data. So, we take samples of 20-year-old people across the country and calculate the average height of the people in samples. According to the Central Limit Theorem, as we take more samples from the population, sampling distribution will get close to a normal distribution.
The central limit theorem is comprised of several key characteristics. These characteristics largely revolve around samples, sample sizes, and the population of data.
Sampling is successive. This means some sample units are common with sample units selected on previous occasions.
Sampling is random. All samples must be selected at random so that they have the same statistical possibility of being selected.
Samples should be independent. The selections or results from one sample should have no bearing on future samples or other sample results.
Samples should be limited. It's often cited that a sample should be no more than 10% of a population if sampling is done without replacement. In general, larger population sizes warrant the use of larger sample sizes.
Sample size is increasing. The central limit theorem is relevant as more samples are selected.

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very informative video