Statistical Inference
Statistical inference is the process of deducing properties of an underlying distribution by analysis of data.
Statistical inference is the process of deducing properties of an underlying distribution by analysis of data.
A confidence intervals is an interval estimate for a distribution's mean \((\mu)\). It is parameterized by a confidence level, which determines how frequently the confidence interval will contain the true distribution mean.
Choose a probability distribution to sample from.
Choose a sample size \((n)\) and confidence level \((1-\alpha)\).
Start sampling to generate confidence intervals.
The p-value is the probability that a statistical summary, such as mean, would be the same as or more extreme than an observed result given a probability distribution for the statistical summary.
Choose a probability distribution for the statistical summary.
Decide what type of p-value to compute.
Type in an observed result to visualize the p-value.
\(p\) =
Switch input from observation to a p-value and visualization computes the critical value....
Not Yet Implemented. Check out the following link for an example of the visualization: Hypothesis Testing
Choose an effect size \((d)\).
Choose type of hypothesis test and set the rejection region by dragging and dropping the critical value(s).
Start sampling... Type I error... Type II error...
| \(H_{0}\) true | \(H_{A}\) true | |
| accept | \(1-\alpha\) | \(\beta\) |
| reject | \(\alpha\) | \(1-\beta\) |