| **sample** | a finite set of selected individuals<br/>assumed to be representative of a population |

Always good to get a reminder about [general considerations](https://www.coursera.org/learn/stanford-statistics/home/welcome), __prior to data collection__ and analysis.

* Sampling from the population,

* identifying the sources of variability...

* identifying the sources of variability, etc.

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In practice, all tests boil down to comparing a single value with a reference distribution. Basically, a test expresses the discrepancy between the observations and the expectation in the shape of a *statistic*, and this statistic is supposed to follow a given distribution under $H_0$.

This is used as a basis to calculate a *p*-value that estimates the probability of erroneously rejecting $H_0$.

The experimenter also defines a significance level $\alpha$, with common values $\alpha=0.05$ or $0.01$, that sets the maximum tolerated risk of rejecting $H_0$ by chance.

The experimenter also defines a significance level $\alpha$, with common values $\alpha=0.05$ or $0.01$, that sets the maximum tolerated risk of making a *type-1 error*, *i.e.* of rejecting $H_0$ by chance.

If the obtained <em>p</em>-value is lower than $\alpha$, then s·he can conclude there is sufficient evidence to reject $H_0$.