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- https://chem.libretexts.org/Courses/Los_Angeles_Trade_Technical_College/Analytical_Chemistry/2%3A_Analytical_Chemistry_2.0_(Harvey)/05%3A_Evaluating_Analytical_Data/5.06%3A_Statistical_Methods_for_Normal_DistributionsThe normal distribution is the most common distribution used for experimental results. Because the area between any two limits of a normal distribution is well defined, constructing and evaluating sig...The normal distribution is the most common distribution used for experimental results. Because the area between any two limits of a normal distribution is well defined, constructing and evaluating significance tests is straightforward.
- https://chem.libretexts.org/Bookshelves/Analytical_Chemistry/Supplemental_Modules_(Analytical_Chemistry)/Data_Analysis/Data_Analysis_II/05_Outliers/01_The_Q-TestThe basis of the Q-test is to compare the difference between the suspected outlier's value and the value of the result nearest to it (the gap) to the difference between the suspected outlier's value a...The basis of the Q-test is to compare the difference between the suspected outlier's value and the value of the result nearest to it (the gap) to the difference between the suspected outlier's value and the value of the result furthest from it the range). The larger the value of Q, the more likely that the suspected outlier does not belong to the same population as the other data points.
- https://chem.libretexts.org/Courses/Lakehead_University/Analytical_I/4%3A_Evaluating_Analytical_Data/4.06%3A_Statistical_Methods_for_Normal_DistributionsThe normal distribution is the most common distribution used for experimental results. Because the area between any two limits of a normal distribution is well defined, constructing and evaluating sig...The normal distribution is the most common distribution used for experimental results. Because the area between any two limits of a normal distribution is well defined, constructing and evaluating significance tests is straightforward.
- https://chem.libretexts.org/Bookshelves/Analytical_Chemistry/Supplemental_Modules_(Analytical_Chemistry)/Data_Analysis/Data_Analysis_II/05_Outliers/02_Problem_1Begin by placing two points about 1 cm apart somewhere in the middle of the one of the lines and placing one point (the possible "outlier") near the right end of the same line. In the box to the right...Begin by placing two points about 1 cm apart somewhere in the middle of the one of the lines and placing one point (the possible "outlier") near the right end of the same line. In the box to the right you will find the calculated value for Q and the probability (P) that rejecting the data point is an error. Starting with a new line in the applet, place three data points in the middle of the line, click on the CALC button and note the results of the Q-test.
- https://chem.libretexts.org/Bookshelves/Analytical_Chemistry/Supplemental_Modules_(Analytical_Chemistry)/Data_Analysis/Data_Analysis_II/05_Outliers/04_Problem_3You can reject one of the possible outliers, the penny from 1943, without a Q-test because you know, from external evidence, that its composition is different from that of the other pennies. If you kn...You can reject one of the possible outliers, the penny from 1943, without a Q-test because you know, from external evidence, that its composition is different from that of the other pennies. If you know that there is a significant error affecting one data point that does not affect other data points, then you should eliminate that data point without regard to whether its value is similar to or different from the remaining data.
- https://chem.libretexts.org/Bookshelves/Analytical_Chemistry/Supplemental_Modules_(Analytical_Chemistry)/Data_Analysis/Data_Analysis_II/05_Outliers/06_Further_StudyOne limitation to our treatment of the outliers is that the Q-table is limited to data sets consisting of 3 to 10 samples. Q-tables are available for larger sample sizes, although, interestingly, the ...One limitation to our treatment of the outliers is that the Q-table is limited to data sets consisting of 3 to 10 samples. Q-tables are available for larger sample sizes, although, interestingly, the test as defined here is less reliable for samples larger than 10. For small data sets, the Q-test as defined will be unable to detect an outlier. For larger data sets, those containing more than 11 samples, there are alternative forms of the Q-test that provides some discriminating ability.
- https://chem.libretexts.org/Bookshelves/Analytical_Chemistry/Supplemental_Modules_(Analytical_Chemistry)/Data_Analysis/Data_Analysis_II/05_OutliersSuppose, for example, that we gather seven replicate sediment samples from a local stream and bring them back to the lab with the intent of determining the concentration of Pb in the sediment. and rep...Suppose, for example, that we gather seven replicate sediment samples from a local stream and bring them back to the lab with the intent of determining the concentration of Pb in the sediment. and report the average (5.8 ppb) as an estimate of the amount of Pb in the sediment and the standard deviation (1.9 ppb) as an estimate of the uncertainty in that result.
