![qxdm 3.09 19 qxdm 3.09 19](https://images-eu.ssl-images-amazon.com/images/I/51QXDmf3H7S._AC_UL160_SR160,160_.jpg)
"In **parametric** inference, we specify *a priori* a suitable distribution, then choose the parameters that best fit the data. "An recurring statistical problem is finding estimates of the relevant parameters that correspond to the distribution that best represents our data.
![qxdm 3.09 19 qxdm 3.09 19](https://i.ytimg.com/vi/auOQjqOekUw/maxresdefault.jpg)
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#QXDM 3.09 19 HOW TO#
This section of the tutorial illustrates how to use Python to build statistical models of low to moderate difficulty from scratch, and use them to extract estimates and associated measures of uncertainty. "A far more powerful approach to statistical analysis involves building flexible **models** with the overarching aim of *estimating* quantities of interest. Even when interpreted correctly, statistical significance is a questionable goal for statistical inference, as it is of limited utility. Such tests seek to esimate whether groups or effects are \"statistically significant \", a concept that is poorly understood, and hence often misused, by most practioners. Unfortunately, the curricula for most introductory statisics courses are mostly focused on conducting **statistical hypothesis tests** as the primary means for interest: t-tests, chi-squared tests, analysis of variance, etc. "Some or most of you have probably taken some undergraduate- or graduate-level statistics courses.