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@ -18,10 +18,7 @@ Welcome to ScienceNotes's documentation!
statistics/probability_distribution_functions.rst statistics/probability_distribution_functions.rst
statistics/distributions/index.rst statistics/distributions/index.rst
statistics/tests_parametric/index.rst statistics/tests/index.rst
statistics/tests_non_parametric/index.rst
Indices and tables Indices and tables

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Statistical Tests
------------------
Statistical tests are used to decide whether a hypotesis is true or not. To do so, two hypothesis must be formulated:
#. :math:`H_0` : The one that is true at first glance (nothing particular, null, boring):
- The mean of two populations are equals
- The average weight of the children in that school is not different from the one of the country
#. :math:`H_1` : The one that says: "Something strange is happening"
- The mean of two populations are differents
- The average weight of the children in that school differ from the one of the country
A statistical test usually works with a **p-value** noted :math:`\alpha`.
It corresponds to the probability of obtaining the results that you have under the assumption that the null hypothesis is correct.
In other words, how lucky you are of obtaining these results.
Prior performing a statistical test, you must choose a minimal *p-value*.
:math:`H_0` will be rejected (meaning :math:`H_1` considered true) if the *p-value* obtained from the statistical test
is lower or equal to the one you choose initially.
The lower your initial *p-value* is, the more difficult it is to reject the null hypothesis.
.. toctree::
:maxdepth: 2
:caption: Categories
parametric/index
non-parametric/index

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@ -2,8 +2,8 @@ Parametric Tests
----------------- -----------------
.. toctree:: .. toctree::
:maxdepth: 2 :maxdepth: 1
:caption: Statistics
ttest
ztest ztest
ttest

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Z-Test
-------
The z-test is used to assess if the mean :math:`\overline{x}` of sample :math:`X` significantly differ from the one of a known population.
The *significance level* is determined by a *p-value* threshold.
Conditions for using a z-test:
#. Population is normally distributed
#. Population :math:`\mu` and :math:`\sigma` is known
#. Sample size is greater than 30 (see note below)
.. note::
According to central limit theorem, a distribution is well approximated when reaching 30 samples.
See `here <https://statisticsbyjim.com/basics/central-limit-theorem/>`__ for more infos.
One-tailed vs Two-tailed
========================
To perform a z-test, you should compute the *standard score* (or *z-score*) of your sample.
It corresponds to the projection of the sample mean :math:`\overline{x}` under the original population distribution.
It is computed as follow:
.. math::
Z=\frac{\overline{x}-\mu}{\sigma}
.. note::
The following formula can also be seen, when the original population :math:`\sigma` is unknown:
.. math::
Z=\frac{\overline{x}-\mu}{\mathrm{SEM}}=\frac{\overline{x}-\mu}{\frac{s}{\sqrt{n}}}
This formula originate from the t-test and :math:`Z` technically follow a t-distribution.
However, if :math:`n` is sufficiently large, the sample distribution is very close to a normal one.
So close that, using the normal in place of the student-t to compute p values leads to nominal differences (`source <https://stats.stackexchange.com/questions/625578/why-is-the-sample-standard-deviation-used-in-the-z-test>`__).
One tailed two tailed:
https://stats.oarc.ucla.edu/other/mult-pkg/faq/general/faq-what-are-the-differences-between-one-tailed-and-two-tailed-tests/
example 2 tailed https://www.mathandstatistics.com/learn-stats/hypothesis-testing/two-tailed-z-test-hypothesis-test-by-hand
Examples
========

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Z-Test
-------
One tailed two tailed:
https://stats.oarc.ucla.edu/other/mult-pkg/faq/general/faq-what-are-the-differences-between-one-tailed-and-two-tailed-tests/
example 2 tailed https://www.mathandstatistics.com/learn-stats/hypothesis-testing/two-tailed-z-test-hypothesis-test-by-hand