Editing Biostatistics (section)

=== Inferential statistics ===
{{Main| Statistical inference}}

It is used to make [[inference]]s<ref>{{Cite journal|title=Essentials of Biostatistics in Public Health & Essentials of Biostatistics Workbook: Statistical Computing Using Excel|journal=Australian and New Zealand Journal of Public Health|volume=33|issue=2|pages=196–197|doi=10.1111/j.1753-6405.2009.00372.x|issn=1326-0200|year=2009|doi-access=free |last1=Watson |first1=Lyndsey }}</ref> about an unknown population, by estimation and/or hypothesis testing. In other words, it is desirable to obtain parameters to describe the population of interest, but since the data is limited, it is necessary to make use of a representative sample in order to estimate them. With that, it is possible to test previously defined hypotheses and apply the conclusions to the entire population. The  [[Standard error|standard error of the mean]] is a measure of variability that is crucial to do inferences.<ref name=":2" />
* [[Statistical hypothesis testing|Hypothesis testing]]

Hypothesis testing is essential to make inferences about populations aiming to answer research questions, as settled in "Research planning" section. Authors defined four steps to be set:<ref name=":2"/>

# ''The hypothesis to be tested'': as stated earlier, we have to work with the definition of a [[null hypothesis]] (H<sub>0</sub>), that is going to be tested, and an [[alternative hypothesis]]. But they must be defined before the experiment implementation.
# ''Significance level and decision rule'': A decision rule depends on the [[significance level|level of significance]], or in other words, the acceptable error rate (α). It is easier to think that we define a ''critical value'' that determines the statistical significance when a [[test statistic]] is compared with it. So, α also has to be predefined before the experiment.
# ''Experiment and statistical analysis'': This is when the experiment is really implemented following the appropriate [[Design of experiments|experimental design]], data is collected and the more suitable statistical tests are evaluated.
# ''Inference'': Is made when the [[null hypothesis]] is rejected or not rejected, based on the evidence that the comparison of [[p-value]]s and α brings. It is pointed that the failure to reject H<sub>0</sub> just means that there is not enough evidence to support its rejection, but not that this hypothesis is true.
* [[Confidence intervals]]

A confidence interval is a range of values that can contain the true real parameter value in given a certain level of confidence. The first step is to estimate the best-unbiased estimate of the population parameter. The upper value of the interval is obtained by the sum of this estimate with the multiplication between the standard error of the mean and the confidence level. The calculation of lower value is similar, but instead of a sum, a subtraction must be applied.<ref name=":2" />