![]() Me personally, I identify more with the 2.0 rule, but 1.96 can certainly indicate some statistical significance! This is because less than 25-percent of the data falls beyond 2 standard deviations from the mean. This is also commonly used in statistical analysis, as it is pretty easy to tell when some statistical significance is present whenever it gets about 1.96 standard deviations from the mean. ![]() This is great for machine-learning, as generally it can bring the variance of the data down a tick and make the data feel closer together. This PDF transforms continuous samples into standard deviations from the population’s mean. Standard scaling of data is quite a popular way to normalize continuous values whose data has high variance. There is another application of what is most likely the most popular PDF used in science, the normal distribution, that is quite commonly used in machine-learning. While the absolute likelihood for a continuous random variable to take on any particular value is 0, the value of the PDF can be used to infer, in any particular sample of random variables, how much more likely it is statistically that the random variable would equal one sample compared to the other sample. This T statistic, along with the degrees of freedom (n minus one) (v,) are then usually put into the regularized lower incomplete beta function, which happens to be the cumulative distribution function for the T distribution. For example, the PDF of the T distribution is often used to calculate a T-statistic. ![]() ![]() However, there are some PDFs that extend beyond this basic usage and have slightly different uses than one might be assume on first glance. Generally, PDFs are a necessary tool when studying data with applied science using statistics. PDFs are very commonly used in statistical analysis, and thus are quite commonly used for Data Science. ![]()
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