# Analysing one sample#

First, we create some random variables. We set a seed so that in each run we get identical results to look at. As an example we take a sample from the Student t distribution:

```
>>> import numpy as np
>>> import scipy.stats as stats
>>> x = stats.t.rvs(10, size=1000)
```

Here, we set the required shape parameter of the t distribution, which
in statistics corresponds to the degrees of freedom, to 10. Using size=1000 means
that our sample consists of 1000 independently drawn (pseudo) random numbers.
Since we did not specify the keyword arguments *loc* and *scale*, those are
set to their default values zero and one.

## Descriptive statistics#

*x* is a numpy array, and we have direct access to all array methods, e.g.,

```
>>> print(x.min()) # equivalent to np.min(x)
-3.78975572422 # random
>>> print(x.max()) # equivalent to np.max(x)
5.26327732981 # random
>>> print(x.mean()) # equivalent to np.mean(x)
0.0140610663985 # random
>>> print(x.var()) # equivalent to np.var(x))
1.28899386208 # random
```

How do the sample properties compare to their theoretical counterparts?

```
>>> m, v, s, k = stats.t.stats(10, moments='mvsk')
>>> n, (smin, smax), sm, sv, ss, sk = stats.describe(x)
```

```
>>> sstr = '%-14s mean = %6.4f, variance = %6.4f, skew = %6.4f, kurtosis = %6.4f'
>>> print(sstr % ('distribution:', m, v, s ,k))
distribution: mean = 0.0000, variance = 1.2500, skew = 0.0000, kurtosis = 1.0000 # random
>>> print(sstr % ('sample:', sm, sv, ss, sk))
sample: mean = 0.0141, variance = 1.2903, skew = 0.2165, kurtosis = 1.0556 # random
```

Note: *stats.describe* uses the unbiased estimator for the variance, while
np.var is the biased estimator.

For our sample the sample statistics differ a by a small amount from their theoretical counterparts.

## T-test and KS-test#

We can use the t-test to test whether the mean of our sample differs in a statistically significant way from the theoretical expectation.

```
>>> print('t-statistic = %6.3f pvalue = %6.4f' % stats.ttest_1samp(x, m))
t-statistic = 0.391 pvalue = 0.6955 # random
```

The pvalue is 0.7, this means that with an alpha error of, for example, 10%, we cannot reject the hypothesis that the sample mean is equal to zero, the expectation of the standard t-distribution.

As an exercise, we can calculate our ttest also directly without using the provided function, which should give us the same answer, and so it does:

```
>>> tt = (sm-m)/np.sqrt(sv/float(n)) # t-statistic for mean
>>> pval = stats.t.sf(np.abs(tt), n-1)*2 # two-sided pvalue = Prob(abs(t)>tt)
>>> print('t-statistic = %6.3f pvalue = %6.4f' % (tt, pval))
t-statistic = 0.391 pvalue = 0.6955 # random
```

The Kolmogorov-Smirnov test can be used to test the hypothesis that the sample comes from the standard t-distribution

```
>>> print('KS-statistic D = %6.3f pvalue = %6.4f' % stats.kstest(x, 't', (10,)))
KS-statistic D = 0.016 pvalue = 0.9571 # random
```

Again, the p-value is high enough that we cannot reject the hypothesis that the random sample really is distributed according to the t-distribution. In real applications, we don’t know what the underlying distribution is. If we perform the Kolmogorov-Smirnov test of our sample against the standard normal distribution, then we also cannot reject the hypothesis that our sample was generated by the normal distribution given that, in this example, the p-value is almost 40%.

```
>>> print('KS-statistic D = %6.3f pvalue = %6.4f' % stats.kstest(x, 'norm'))
KS-statistic D = 0.028 pvalue = 0.3918 # random
```

However, the standard normal distribution has a variance of 1, while our sample has a variance of 1.29. If we standardize our sample and test it against the normal distribution, then the p-value is again large enough that we cannot reject the hypothesis that the sample came form the normal distribution.

```
>>> d, pval = stats.kstest((x-x.mean())/x.std(), 'norm')
>>> print('KS-statistic D = %6.3f pvalue = %6.4f' % (d, pval))
KS-statistic D = 0.032 pvalue = 0.2397 # random
```

Note: The Kolmogorov-Smirnov test assumes that we test against a distribution with given parameters, since, in the last case, we estimated mean and variance, this assumption is violated and the distribution of the test statistic, on which the p-value is based, is not correct.

## Tails of the distribution#

Finally, we can check the upper tail of the distribution. We can use the percent point function ppf, which is the inverse of the cdf function, to obtain the critical values, or, more directly, we can use the inverse of the survival function

```
>>> crit01, crit05, crit10 = stats.t.ppf([1-0.01, 1-0.05, 1-0.10], 10)
>>> print('critical values from ppf at 1%%, 5%% and 10%% %8.4f %8.4f %8.4f' % (crit01, crit05, crit10))
critical values from ppf at 1%, 5% and 10% 2.7638 1.8125 1.3722
>>> print('critical values from isf at 1%%, 5%% and 10%% %8.4f %8.4f %8.4f' % tuple(stats.t.isf([0.01,0.05,0.10],10)))
critical values from isf at 1%, 5% and 10% 2.7638 1.8125 1.3722
```

```
>>> freq01 = np.sum(x>crit01) / float(n) * 100
>>> freq05 = np.sum(x>crit05) / float(n) * 100
>>> freq10 = np.sum(x>crit10) / float(n) * 100
>>> print('sample %%-frequency at 1%%, 5%% and 10%% tail %8.4f %8.4f %8.4f' % (freq01, freq05, freq10))
sample %-frequency at 1%, 5% and 10% tail 1.4000 5.8000 10.5000 # random
```

In all three cases, our sample has more weight in the top tail than the underlying distribution. We can briefly check a larger sample to see if we get a closer match. In this case, the empirical frequency is quite close to the theoretical probability, but if we repeat this several times, the fluctuations are still pretty large.

```
>>> freq05l = np.sum(stats.t.rvs(10, size=10000) > crit05) / 10000.0 * 100
>>> print('larger sample %%-frequency at 5%% tail %8.4f' % freq05l)
larger sample %-frequency at 5% tail 4.8000 # random
```

We can also compare it with the tail of the normal distribution, which has less weight in the tails:

```
>>> print('tail prob. of normal at 1%%, 5%% and 10%% %8.4f %8.4f %8.4f' %
... tuple(stats.norm.sf([crit01, crit05, crit10])*100))
tail prob. of normal at 1%, 5% and 10% 0.2857 3.4957 8.5003
```

The chisquare test can be used to test whether for a finite number of bins, the observed frequencies differ significantly from the probabilities of the hypothesized distribution.

```
>>> quantiles = [0.0, 0.01, 0.05, 0.1, 1-0.10, 1-0.05, 1-0.01, 1.0]
>>> crit = stats.t.ppf(quantiles, 10)
>>> crit
array([ -inf, -2.76376946, -1.81246112, -1.37218364, 1.37218364,
1.81246112, 2.76376946, inf])
>>> n_sample = x.size
>>> freqcount = np.histogram(x, bins=crit)[0]
>>> tprob = np.diff(quantiles)
>>> nprob = np.diff(stats.norm.cdf(crit))
>>> tch, tpval = stats.chisquare(freqcount, tprob*n_sample)
>>> nch, npval = stats.chisquare(freqcount, nprob*n_sample)
>>> print('chisquare for t: chi2 = %6.2f pvalue = %6.4f' % (tch, tpval))
chisquare for t: chi2 = 2.30 pvalue = 0.8901 # random
>>> print('chisquare for normal: chi2 = %6.2f pvalue = %6.4f' % (nch, npval))
chisquare for normal: chi2 = 64.60 pvalue = 0.0000 # random
```

We see that the standard normal distribution is clearly rejected, while the standard t-distribution cannot be rejected. Since the variance of our sample differs from both standard distributions, we can again redo the test taking the estimate for scale and location into account.

The fit method of the distributions can be used to estimate the parameters of the distribution, and the test is repeated using probabilities of the estimated distribution.

```
>>> tdof, tloc, tscale = stats.t.fit(x)
>>> nloc, nscale = stats.norm.fit(x)
>>> tprob = np.diff(stats.t.cdf(crit, tdof, loc=tloc, scale=tscale))
>>> nprob = np.diff(stats.norm.cdf(crit, loc=nloc, scale=nscale))
>>> tch, tpval = stats.chisquare(freqcount, tprob*n_sample)
>>> nch, npval = stats.chisquare(freqcount, nprob*n_sample)
>>> print('chisquare for t: chi2 = %6.2f pvalue = %6.4f' % (tch, tpval))
chisquare for t: chi2 = 1.58 pvalue = 0.9542 # random
>>> print('chisquare for normal: chi2 = %6.2f pvalue = %6.4f' % (nch, npval))
chisquare for normal: chi2 = 11.08 pvalue = 0.0858 # random
```

Taking account of the estimated parameters, we can still reject the hypothesis that our sample came from a normal distribution (at the 5% level), but again, with a p-value of 0.95, we cannot reject the t-distribution.

## Special tests for normal distributions#

Since the normal distribution is the most common distribution in statistics, there are several additional functions available to test whether a sample could have been drawn from a normal distribution.

First, we can test if skew and kurtosis of our sample differ significantly from those of a normal distribution:

```
>>> print('normal skewtest teststat = %6.3f pvalue = %6.4f' % stats.skewtest(x))
normal skewtest teststat = 2.785 pvalue = 0.0054 # random
>>> print('normal kurtosistest teststat = %6.3f pvalue = %6.4f' % stats.kurtosistest(x))
normal kurtosistest teststat = 4.757 pvalue = 0.0000 # random
```

These two tests are combined in the normality test

```
>>> print('normaltest teststat = %6.3f pvalue = %6.4f' % stats.normaltest(x))
normaltest teststat = 30.379 pvalue = 0.0000 # random
```

In all three tests, the p-values are very low and we can reject the hypothesis that the our sample has skew and kurtosis of the normal distribution.

Since skew and kurtosis of our sample are based on central moments, we get exactly the same results if we test the standardized sample:

```
>>> print('normaltest teststat = %6.3f pvalue = %6.4f' %
... stats.normaltest((x-x.mean())/x.std()))
normaltest teststat = 30.379 pvalue = 0.0000 # random
```

Because normality is rejected so strongly, we can check whether the normaltest gives reasonable results for other cases:

```
>>> print('normaltest teststat = %6.3f pvalue = %6.4f' %
... stats.normaltest(stats.t.rvs(10, size=100)))
normaltest teststat = 4.698 pvalue = 0.0955 # random
>>> print('normaltest teststat = %6.3f pvalue = %6.4f' %
... stats.normaltest(stats.norm.rvs(size=1000)))
normaltest teststat = 0.613 pvalue = 0.7361 # random
```

When testing for normality of a small sample of t-distributed observations and a large sample of normal-distributed observations, then in neither case can we reject the null hypothesis that the sample comes from a normal distribution. In the first case, this is because the test is not powerful enough to distinguish a t and a normally distributed random variable in a small sample.