It is sometimes useful to imagine that the data we observe is just the tip of an iceberg. If you get into this mindset, then you probably will want to know how big this iceberg actually is. Obviously, if you can't see the whole thing, you can still try to extrapolate from the data you have. In statistics we try to estimate confidence intervals, which are an estimated range usually associated with a certain confidence level quoted in percentages.
The scipy.stats.bayes_mvs() function estimates confidence intervals for mean, variance, and standard deviation. The function uses Bayesian statistics to estimate confidence assuming that the data is independent and normally distributed. Jackknifing is an alternative deterministic algorithm to estimate confidence intervals. It falls under the family of resampling algorithms. Usually, we generate new datasets under the jackknifing algorithm by deleting one value (we can also...