Glossary of Terms
All complex subjects have their own terminology that sometimes makes it hard for new people to break into the field. This sometimes includes uncommon words, but more often than not a subject will have very specific meanings for common words - the discussion of errors vs mistakes in this video is a good example of this.
This glossary is a reference of some of the uncommon terms and specific definitions of more common words that you will encounter throughout Data Tree and your broader dealings with data.
Many of these definitions come from the course materials and experts that helped develop Data Tree. Others come from the CASRAI Dictionary. Those definitions are kindly made available under a Creative Commons Attribution 4.0 International License.
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Testing hypotheses is a common part of statistical inference. To formulate a test, the question of interest is simplified into two competing hypotheses, between which we have a choice. The first is the null hypothesis, denoted by H0, against the alternative hypothesis, denoted by H1 . For example with 50 years of annual rainfall totals a hypothesis test could be whether the mean is different in El Nino and Ordinary years. Then usually • The null hypothesis, H0, is that the two means are equal, i.e. there is no difference. • The alternative hypothesis, H1, is that the two means are unequal, i.e. there is a difference. If the 50 years were considered as being of three types, El Nino, Ordinary, La Nina then usually: • The null hypothesis, H0, is that all three means are equal. • The alternative hypothesis, H1, is that there is a difference somewhere between the means. The hypotheses are often statements about population parameters. In the first example above it might be: • H0, is that µE = µO. • H1, is that µE ≠ µO. The outcome of a hypothesis test is either • Reject H0 in favour of H1, or • Do not reject H0.