Who said that IR and LSI cannot be fun? Detecting Cyberbullying: Query Terms and Techniques
As part of the development of Minerazzi, we have published an article explaining two of our search modes: XOR and XNOR. Additional articles explaining other modes will soon follow.
We believe that IR and SEO practitioners will find these search modes particularly useful.
The beauty of XOR and XNOR searches is that these allow users to run complex co-occurrence searches in a straightforward manner. This is important as Latent Semantic Indexing information is related to term-term co-occurrence relationships.
As we keep putting the final touches to Minerazzi, we have upgraded the article on its search operators to a series of articles. The first of the series can be found here http://www.minerazzi.com/help/search-modes.php
We have taken the time to explain the difference between search modes and their complements using some Venn Diagrams.
In a nutshell, because most are based on flawed statistics.
The Question of Standard Deviations and Variances
If you have studied for the College Board Examination, you should know that standard deviations are not additive. You should also know that variances are additive for independent random variables. Read the article Why Variances Add — And Why It Matters. Many SEOs fail to know this.
The Question of Correlation Coefficients
Like standard deviations, correlation coefficients are not additive, period. Since they cannot be added, it is not possible to compute an arithmetic average out of them. The same can be said about cosines, cosine similarities, slopes, and in general about any dissimilar ratio. Read the Communications in Statistics article The Self-Weighting Model wherein flaws in the top two main meta-analysis models are documented. Again, many SEOs do not understand this point.
The Question of Normality
Although no data set is exactly normally distributed, most statistical analyses require that the data be approximately normally distributed for their findings to be valid; otherwise one cannot claim that, for instance a computed arithmetic mean (average) is a valid estimator of central tendency for the data at hand. Most SEOs and some “web analytic gurus” out there simply take some data and average them without first doing a normality test.
The Question of Big Data and the t-Test of Significance
When the Fathers of Statistics (Fisher and company) came up with the t-test of significance and similar tests, these were meant to be used with small data sets, not big data sets. To illustrate, if you take a very very very large data set of N paired results, compute a statistic (eg. a correlation coefficient), and compare it against a t-table value, eventually it will pass the test of significance. This will be true for experimental correlations as small as 0.1, 0.01, 0.001….. provided that N is large enough. Claims of statistical significancies are in this case useless. This is why with big data you should try data stratification methods, followed by weighting methods. Big data can lead to big statistical pitfalls.
The Question of Average of Ratios or Ratio of Averages
Ratios cannot be added and then averaged arithmetically, period. A ratio of averages must be used instead of computing an average of ratios. The reason is that a ratio distribution is Cauchy. A Cauchy Distribution is often mistaken for a normal one, but has no mean, variance, or higher moments. As more sample are taken, the sample mean and variance change with an increasing bias as more samples are taken. Computing an average mean from a Cauchy distribution is not an estimate of central tendency. SEOs should know what they are averaging. Check one of my old posts and the comments that followed at
To sum up, beware of SEO statistical “studies”.