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by DynamicWebPaige on 2018-09-27 (UTC).

πŸ““ Am rereading my class notes from grad school, as well as from mentoring students for @Coursera and @EdX courses on statistics - and thought I'd share the most common mistakes when doing data analysis.

✨Have counted 8 of 'em, with examples - please feel free to add your own!

β€” πŸ‘©β€πŸ’» @DynamicWebPaige πŸ”œ #APICityConf πŸŒ‡ (@DynamicWebPaige) September 27, 2018
tiplearning
by DynamicWebPaige on 2018-09-27 (UTC).

MISTAKE #1:
Garbage in, garbage out.

πŸ€¦β€β™€οΈFailing to investigate your input for data entry or recording errors.

πŸ“ŠFailing to graph data and calculate basic descriptive statistics (mean, median, mode, outliers, etc.) before analyzing it in-depth. pic.twitter.com/DuBSAw7qJT

β€” πŸ‘©β€πŸ’» @DynamicWebPaige πŸ”œ #APICityConf πŸŒ‡ (@DynamicWebPaige) September 27, 2018
tip
by DynamicWebPaige on 2018-09-27 (UTC).

MISTAKE #3
Implementing experiments that are poorly designed. πŸ”¬

πŸ“ŠStudy doesn't have enough power to call meaningful differences statistically significant.

πŸ‘ŽIncludes concluding that the null hypothesis is true - should be "not enough evidence to say that the null is false".

β€” πŸ‘©β€πŸ’» @DynamicWebPaige πŸ”œ #APICityConf πŸŒ‡ (@DynamicWebPaige) September 27, 2018
tip

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