Week #37 – Truncation

Truncation, generally speaking, means to shorten. In statistics it can mean the process of limiting consideration or analysis to data that meet certain criteria (for example, the patients still alive at a certain point). Or it can refer to a data distribution where values above…

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Week #36 – Tukey´s HSD (Honestly Significant Differences) Test

This test is used for testing the significance of unplanned pairwise comparisons. When you do multiple significance tests, the chance of finding a "significant" difference just by chance increases. Tukey´s HSD test is one of several methods of ensuring that the chance of finding a…

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Week #35 – Robust Filter

A robust filter is a filter that is not sensitive to input noise values with extremely large magnitude (e.g. those arising due to anomalous measurement errors). The median filter is an example of a robust filter. Linear filters are not robust - their output may…

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Week #34 – Hypothesis Testing

Hypothesis testing (also called "significance testing") is a statistical procedure for discriminating between two statistical hypotheses - the null hypothesis (H0) and the alternative hypothesis ( Ha, often denoted as H1). Hypothesis testing, in a formal logic sense, rests on the presumption of validity of the null hypothesis - that is, the null hypothesis is rejected only if the data at hand testify strongly enough against it.

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Week #33 – Kurtosis

Kurtosis measures the "heaviness of the tails" of a distribution (in compared to a normal distribution). Kurtosis is positive if the tails are "heavier" then for a normal distribution, and negative if the tails are "lighter" than for a normal distribution. The normal distribution has kurtosis of zero.

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Week #32 – False Discovery Rate

A "discovery" is a hypothesis test that yields a statistically significant result. The false discovery rate is the proportion of discoveries that are, in reality, not significant (a Type-I error). The true false discovery rate is not known, since the true state of nature is not known (if it were, there would be no need for statistical inference).

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