Changes

== Limitations ==

== Limitations ==

Many forms of statistical testing only aim to answer whether it is likely that any observed differences, correlations etc. are ''statistically significant'', that is how unlikely is it these results are the outcome of chance. These don't say how ''significant'' in a broader sense – for example for the impact or effect size – the results are. In [[genetic testing]] extreme examples of this can be found: Individual genetic variants can be strongly statistically significant which are very unlikely to be the result of chance. At the same time the observed effect of a genetic variant frequently only increases e.g. the risk for a disease by a fraction of a percent.  

Many forms of statistical testing only aim to answer whether it is likely that any observed differences, correlations etc. are ''statistically significant'', that is how unlikely is it these results are the outcome of chance. These don't say how ''significant'' in a broader sense – for example for the impact or effect size – the results are<ref>https://atrium.lib.uoguelph.ca/xmlui/bitstream/handle/10214/1869/A_Statistical_versus_Practical_Significance.pdf</ref>. In [[genetic testing]] extreme examples of this can be found: Individual genetic variants can be strongly statistically significant which are very unlikely to be the result of chance. At the same time the observed effect of a genetic variant frequently only increases e.g. the risk for a disease by a fraction of a percent.  

As personal science is often concerned with observing effects that have a real-world impact, statistical testing and statistical significance alone should be considered only one tool in the personal science toolkit. [[Data visualization|Visualizing data]] can be a very powerful first step to explore if there are visible differences or correlations in ones data. If those visualizations seem to show a large effect, statistical testing can be used to evaluate how 'trustworthy' those effects are.  

As personal science is often concerned with observing effects that have a real-world impact, statistical testing and statistical significance alone should be considered only one tool in the personal science toolkit. [[Data visualization|Visualizing data]] can be a very powerful first step to explore if there are visible differences or correlations in ones data. If those visualizations seem to show a large effect, statistical testing can be used to evaluate how 'trustworthy' those effects are.