# Astrostatistics Tools (astropy.stats)¶

## Introduction¶

The astropy.stats package holds statistical functions or algorithms used in astronomy and astropy.

## Getting Started¶

The current tools are fairly self-contained, and include relevant examples in their docstrings.

## Reference/API¶

### astropy.stats Package¶

This subpackage contains statistical tools provided for or used by Astropy.

While the scipy.stats package contains a wide range of statistical tools, it is a general-purpose package, and is missing some that are particularly useful to astronomy or are used in an atypical way in astronomy. This package is intended to provide such functionality, but not to replace scipy.stats if its implementation satisfies astronomers’ needs.

#### Functions¶

 binned_binom_proportion(x, success[, bins, ...]) Binomial proportion and confidence interval in bins of a continuous variable x. binom_conf_interval(k, n[, conf, interval]) Binomial proportion confidence interval given k successes, n trials. biweight_location(a[, c, M]) Compute the biweight location for an array. biweight_midvariance(a[, c, M]) Compute the biweight midvariance for an array. bootstrap(data[, bootnum, samples, bootfunc]) Performs bootstrap resampling on numpy arrays. mad_std(data) Calculate a robust standard deviation using the median absolute deviation (MAD). median_absolute_deviation(a[, axis]) Compute the median absolute deviation. sigma_clip(data[, sig, iters, cenfunc, ...]) Perform sigma-clipping on the provided data. sigma_clipped_stats(data[, mask, mask_val, ...]) Calculate sigma-clipped statistics from data. signal_to_noise_oir_ccd(t, source_eps, ...) Computes the signal to noise ratio for source being observed in the optical/IR using a CCD.