chemistry

Testing for Normality of Distribution (the Kolmogorov-Smirnov test) Home > Statistics – Frequency Distributions, Normal Distribution, z-scores > One-Sample T-Test in Chemical Analysis – Statistical Treatment of Analytical Data > Testing for Normality of Distribution (the Kolmogorov-Smirnov test) Testing for Normality of Distribution (Kolmogorov-Smirnov test) using the Online Normal Distribution Calculator Many statistical tests (t-test, f-test, one-way ANalysis Of VAriance ANOVA ) assume that data used are drawn from a normal population . Although the chi-squared test can be used to test this assumption it should be used only if there are 50 or more data points , so it is of limited value in analytical work, when we often have only a small set of data. There Kolmogorov-Smirnov test ( a nonparametric test ). The Kolmogorov-Smirnov test – included in SPSS - has been used in a previous post entitled " Statistical Treatm

Dixon' Q test calculator Dixon's Q-test Calculator - Detection of a single outlier   Dixon’s test (or the Q-test ) has been described in a previous post entitled “ Detection of a Single Outlier|Statistical Analysis|Quantitative Data ”. The test is popular because the calculations involved are simple. A solved example is given in the above post. Are there any limitations to Dixon’s Q-test? The data excluding  the possible outlier must be normally distributed (use the Kolmogorov-Smirnov test to check if data is normally distributed ) The Q-test is valid for the detection of a single outlier (it cannot be used for a second time on the same set of data). Other forms of Dixon’s Q-test can be applied to the detection of multiple outliers. The Q-test should be applied with caution – the same applies to all statistical tests used for rejecting data - since there is a probability, equal to the significance level a (a =0.05 at the 95% confidenc