The p value is the probability of the experiment having given the result it did purely by chance. 
The threshold of significance is almost always 0.05 so that if the probability of a chance outcome is less than 1 in 20, the experimental result is declared to be significant. There are other important thresholds as summarised in the following table

p value

Having devised an experiment that is simple to analyse, it is important also not to fall at the final hurdle of interpreting the results of the statistical tests. 

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It is essential for an experiment to succeed. 
Statistics are needed to help identify differences outcome between testing conditions are systematic and meaningfully related to the independent variable.

Statistical analysis

Given the challenges of setting up an experiment as a severe test and the narrowness of the results it can provide, there is a fall-back position for researchers, namely, that experiments can be exploring the possible influences of many factors on particular interactions or experiential outcomes. statistics cannot possibly provide evidence in support of any particular factor influencing the outcome of the experiment. What statistics can indicate is where some results are perhaps more unlikely than might be expected and therefore worthy of further investigation.


Statistics alone do not provide good evidence

Knowing there are pretty dangerous pitfalls in statistical analysis and that statistics is a challenging area to master: so, it should follow keep it simple stupid (KISS). Simpler experiments are actually more severe tests because there is nowhere to hide: something either clearly passes the test or it does not.

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