The sampling distribution is the likelihood of gathering every possible statistic from a sample of a population that is taken randomly. Useful information can be derived using the sampling distribution without the complete knowledge of the population. Suppose we are calculating the sample mean but we don't know the population. Still, we can assume that the sample mean is within a certain number of standard deviations of the population mean.
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Anshul Joshi is a data scientist with experience in recommendation systems, predictive modeling, neural networks, and high performance computing. His research interests encompass deep learning, artificial intelligence, and computational physics. Most of the time, he can be caught exploring GitHub or trying anything new he can get his hands on. You can also follow his personal blog.
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Anshul Joshi is a data scientist with experience in recommendation systems, predictive modeling, neural networks, and high performance computing. His research interests encompass deep learning, artificial intelligence, and computational physics. Most of the time, he can be caught exploring GitHub or trying anything new he can get his hands on. You can also follow his personal blog.
Read more about Anshul Joshi