## FAQ: What are the differences between one-tailed and two-tailed tests

In many situations, the likelihood of two events occurring is not independent. This does not mean that the two events need be totally interdependent or mutually exclusive, just that one event occurring may increase or decrease the likelihood of the other. Put another way, having prior knowledge of one outcome may change the effective probability of a second outcome. Knowing that someone has a well-worn “1997 International Meeting” t-shirt in his drawer does not guarantee that he is an aging nerd, but it certainly does increase the probability! The area of statistics that handles such situations is known as or inference, after an early pioneer in this area, Thomas Bayes. More generally, refers to the probability of an event occurring based on the condition that another event has occurred. Although conditional probabilities are extremely important in certain types of biomedical and epidemiological research, such as predicting disease states given a set of known factors, this issue doesn't arise too often for most researchers. Bayesian models and networks have, however, been used in the worm field for applications that include phylogenetic gene tree construction (; ), modeling developmental processes (), and predicting genetic interactions (). Bayesian statistics is also used quite extensively in behavioral neuroscience (; ), which is growing area in the field. We refer interested readers to textbooks or the web for additional information (see ).

### One- and two-tailed tests - Wikipedia

The extent of correlation between two variables can be quantified through calculation of a statistical parameter termed the (a.k.a. , , or just ). The formula is a bit messy and the details are not essential for interpretation. The value of can range from −1 (a perfect negative correlation) to 1 (a perfect positive correlation), or can be 0 in the case of no correlation. Thus, depending on the tightness of the correlation, values will range from close to zero (weak or no correlation) to 1 or −1 (perfect correlation). In our example, if one of the two genes encodes a transcriptional activator of the other gene, we would expect to see a positive correlation. In contrast, if one of the two genes encodes a repressor, we should observe a negative correlation. If expression of the two genes is in no way connected, should be close to zero, although random chance would likely result in having either a small positive or negative value. Even in cases where a strong correlation is observed, however, it is important not to make the common mistake of equating correlation with causation.

Data from studies where relative or exponential changes are pervasive may also benefit from transformation to log scales. For example, transforming to a log scale is the standard way to obtain a straight line from a slope that changes exponentially. This can make for a more straightforward presentation and can also simplify the statistical analysis (see on outliers). Thus, transforming 1, 10, 100, 1,000 into log10 gives us 0, 1, 2, 3. Which log base you choose doesn't particularly matter, although ten and two are quite intuitive, and therefore popular. The natural log (∼2.718), however, has historical precedent within certain fields and may be considered standard. In some cases, back transformation (from log scale to linear) can be done after the statistical analysis to make the findings clearer to readers.

## 03/01/2018 · One- and Two-Tailed Tests ..

Very often we will want to compare two proportions for differences. For example, we may observe 85% larval arrest in mutants grown on control RNAi plates and 67% arrest in mutants on RNAi-feeding plates targeting gene . Is this difference significant from a statistical standpoint? To answer this, two distinct tests are commonly used. These are generically known as the and methods. In fact, many website calculators or software programs will provide the -value calculated by each method as a matter of course, although in some cases you may need to select one method. The approximation method (based on the so-called normal distribution) has been in general use much longer, and the theory behind this method is often outlined in some detail in statistical texts. The major reason for the historical popularity of the approximation method is that prior to the advent of powerful desktop computers, calculations using the exact method simply weren't feasible. Its continued use is partly due to convention, but also because the approximation and exact methods typically give very similar results. Unlike the normal approximation method, however, the exact method is valid in all situations, such as when the number of successes is less than five or ten, and can thus be recommended over the approximation method.

## One and Two Tailed Tests | Statistical Hypothesis …

From this we can see that either two or three F2s is the most efficient use of plates and possibly time, although other factors could potentially factor into the decision of how many F2s to pick. We can also see that the are independent of the frequency of the mutation of interest. Importantly, this potentially useful insight was accomplished using basic intuition and a very rudimentary knowledge of probabilities. Of course, the outlined intuitive approach failed to address whether the optimal number of cloned F2s is 2.4 or 2.5, but as we haven't yet developed successful methods to pick or propagate fractions of , such details are irrelevant!