Pseudoreplication. Pseudoreplication, as originally defined[1] , is a special case of inadequate specification of random factors where both random and fixed factors are present.[2] The problem described by this term arises when treatments are assigned to units that are subsampled and the treatment F-ratio in an analysis of variance (ANOVA) table is formed with respect to the residual mean square rather than with respect to the among unit mean square.
The F-ratio relative to the within unit mean square is vulnerable to the confounding of treatment and unit effects, especially when unit number is small (e.g. four tank units, two tanks treated, two not treated, several subsamples per tank). The problem is eliminated by forming the F-ratio relative to the among unit mean square in the ANOVA table (tank MS in the example above). Replication[edit] Hypothesis testing[edit] Statistical tests (e.g. t-test and the related ANOVA family of tests) rely on appropriate replication to estimate statistical confidence.
Misuse of statistics. A misuse of statistics occurs when a statistical argument asserts a falsehood.
In some cases, the misuse may be accidental. In others, it is purposeful and for the gain of the perpetrator. When the statistical reason involved is false or misapplied, this constitutes a statistical fallacy. The false statistics trap can be quite damaging to the quest for knowledge. For example, in medical science, correcting a falsehood may take decades and cost lives. Misuses can be easy to fall into. Types of misuse[edit] Discarding unfavorable data[edit] All a company has to do to promote a neutral (useless) product is to find or conduct, for example, 40 studies with a confidence level of 95%.
Another common technique is to perform a study that tests a large number of dependent (response) variables at the same time. Loaded questions[edit] The answers to surveys can often be manipulated by wording the question in such a way as to induce a prevalence towards a certain answer from the respondent. Misuse of statistics. A misuse of statistics occurs when a statistical argument asserts a falsehood.
In some cases, the misuse may be accidental. In others, it is purposeful and for the gain of the perpetrator. When the statistical reason involved is false or misapplied, this constitutes a statistical fallacy. The false statistics trap can be quite damaging to the quest for knowledge. For example, in medical science, correcting a falsehood may take decades and cost lives. Misuses can be easy to fall into. Types of misuse[edit] Discarding unfavorable data[edit] All a company has to do to promote a neutral (useless) product is to find or conduct, for example, 40 studies with a confidence level of 95%. Another common technique is to perform a study that tests a large number of dependent (response) variables at the same time.
Loaded questions[edit] The answers to surveys can often be manipulated by wording the question in such a way as to induce a prevalence towards a certain answer from the respondent. Is correct. Common statistical fallacies. I've been reading papers on how people learn statistics (and thoughts on teaching the subject) and came across the frequently-cited work of mathematical psychologists Amos Tversky and Daniel Kahneman.
In 1972, they studied statistical misconceptions. It doesn't seem much has changed. Joan Garfield (1995) summarizes in How to Learn Statistics [pdf]. Representativeness: People estimate the likelihood of a sample based on how closely it resembles the population. You can't always judge how likely or improbable a sample is based on how it compares to a known population. Similarly, a sequence of ten heads in a row isn't the same as getting a million heads in a row. Gambler's fallacy: Use of the representative heuristic leads to the view that chance is a self-correcting process. The history boards at roulette tables mean nothing. Base-rate fallacy: People ignore the relative sizes of population subgroups when judging the likelihood of contingent events involving the subgroups.
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