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The problem with unadjusted multiple and sequential statistical testing

In most Statistical Analysis, researchers often wish to get sufficient power to balance the cost spent for the experiment such as in medical experiment. The most common statistical technique is that using sequential sampling of data until the desired condition is satisfied. However, using this technique leads to an inflated rate of type I and type II error rate. In this blog, the Statistical Method which deals with the sequential sampling procedure are discussed. Statswork offers statistical services as per the requirements of the customers. When you Order statistical Services at Statswork, we promise you the following u2013 Always on Time, outstanding customer support, and High-quality Subject Matter Experts<br><br>Contact Us:<br><br>Website: www.statswork.com<br><br>Email: info@statswork.com<br><br>UnitedKingdom: 44-1143520021<br><br>India: 91-4448137070<br>t<br>WhatsApp: 91-8754446690<br>

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The problem with unadjusted multiple and sequential statistical testing

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  1. PROBLEMWITH UNADJUSTEDMULTIPLEAND SEQUENTIALSTATISTICAL TESTING Tags: Statswork | Statistical Analysis | Statistical Method | Sample Size Significance | Sequential Analysis | Data Collection | Interim Analysis Research Planing | Data Collection | Semantic Annotation | Consumer & Retail Analytics | Econometrics Copyright © 2019 Statswork. All rights reserved

  2. - Large number of statistical tests are performed, then there will be a chance of increased false positive rates or there will be the problem of multiple testing for the sample considered. - Bonferroni correction will be carried out to deal with the multiple testing problems without making any adjustments. - This Bonferroni correction have serious drawback. Bonferroni correction Research Planing | Data Collection | Semantic Annotation | Consumer & Retail Analytics | Econometrics Copyright © 2019 Statswork. All rights reserved

  3. DRAWBACKSOFBONFERRONI CORRECTION We perform multiple independent tests, then the probability or chance of getting atleast one false positive is calculated as 1-(1-0.05)^n. Suppose if n=10, then the probability will be 40.14 %, which is very high. In such situations, the use of Bonferroni correction is not appropriate. Research Planing | Data Collection | Semantic Annotation | Consumer & Retail Analytics | Econometrics Copyright © 2019 Statswork. All rights reserved

  4. Sequential testing problem is an alternative to cope up with the multiple testing problems. Sequential testing means the researchers collect the data until we reach the fixed threshold. It takes more effort, time and it's expensive in practice. One can check the decreasing p-value when the samples are tested sequentially. Sequential testing Research Planing | Data Collection | Semantic Annotation | Consumer & Retail Analytics | Econometrics Copyright © 2019 Statswork. All rights reserved

  5. UncorrectedMultiple TestingProcedure Uncorrected multiple testing procedure, one would impose the stopping rule, say, stop the process once the false positive rate reaches 25%. 01 This procedure seems comfortable, it will have an impact on the estimated values. 02 03 In the same way, sequential testing problem have a serious drawback. When we do sampling sequentially, researchers often face an effect of over estimates. 04 05 Effect size is also result in bias nature. Research Planing | Data Collection | Semantic Annotation | Consumer & Retail Analytics | Econometrics Copyright © 2019 Statswork. All rights reserved

  6. ProblemofSequential From the graph, it is noted that the sequential testing (blue curve) is less severe than the uncorrelated multiple testing (red curve). Sample size significance for the simulated 10000 sequential strategies. andMultipleTesting If, we impose any stopping rule also it will exceed the limit and gives a false discovery rate. This kind of testing affects the estimated values apart from the probability values. Sequential sampling, distance between both group means will increase or decrease and if one wish to continue the process of sampling till both groups yields significant results. Hence, the sequential testing is biased in significance and also in effect size. Research Planing | Data Collection | Semantic Annotation | Consumer & Retail Analytics | Econometrics Copyright © 2019 Statswork. All rights reserved

  7. ProblemofUnadjustedSequential Testing If we sample the data sequentially in smaller bits and achieve the fixed limit means we actually increasing the sample size to attain our goal. Concept of sequential testing is actually a great idea only if we make necessary corrections to make the sample to be larger in size. Research Planing | Data Collection | Semantic Annotation | Consumer & Retail Analytics | Econometrics Copyright © 2019 Statswork. All rights reserved

  8. Groupsequentialanalysisorinterim analysistheresearcherhavetomake anpriorispecificationsaboutthedata. Forinstance, oneshouldmakethe Group priordecisionthatthesamplesshould betakenas50samplesinfirstlevel, Sequential Analysis 100insecondlevel, etc., andstops whenthedesiredresultisobtained. Mainadvantageofthistechniqueis thatonecanstopthedata collection whenthedesiredlevelisobtained. Research Planing | Data Collection | Semantic Annotation | Consumer & Retail Analytics | Econometrics Copyright © 2019 Statswork. All rights reserved

  9. Fullsequentialtechnique, thereisnoprior arrangementsisneeded. FULL SEQUENTIAL TECHNIQUE Inearly1940s, Waldsusedthistechniquein computingthecumulativelog-likelihoodratiofor eachobservationcollectedandstopstheprocess whenapre-definedthresholdisachieved. Thisissomethinglikethecaseininterimanalysis. However, thefullsequentialtechniqueisnot practical. Supposeifaresearcherwantstoanalysethesample of20grouptherapyparticipants, thenthismaynot beappropriatebutthegroupsequentialanalysis willservesapurpose. Research Planing | Data Collection | Semantic Annotation | Consumer & Retail Analytics | Econometrics Copyright © 2019 Statswork. All rights reserved

  10. CONCLUSION Description Method Sample size needed S.No 1. Non-sequential analysis It collects a single sample and perform the analysis in later stage. It's an straight forward method but has an disadvantage is that one might collect more data than necessary. Large 2. Group sequential analysis It is also called an interim analysis which make use of a priori decisions for the analysis and stops when significance is reached. Moderate 3. Full sequential analysis Unlike the above case, it does not requires a prior specifications. It computes a statistical analysis based on the sample once the observation is recorded and stops data collection when it lies outside the specified limit. Low Research Planing | Data Collection | Semantic Annotation | Consumer & Retail Analytics | Econometrics Copyright © 2019 Statswork. All rights reserved

  11. Statswork Lab @ Statswork.com www.statswork.com Research Planing | Data Collection | Semantic Annotation | Consumer & Retail Analytics | Econometrics Copyright © 2019 Statswork. All rights reserved

  12. PHONE NUMBER UK : +44-1143520021 INDIA : +91-4448137070 Freelancer EMAIL ADDRESS info@statswork.com Consultant Guest Blog Editor GET IN TOUCH WITH US CONTACT hr@workfoster.com Research Planing | Data Collection | Semantic Annotation | Consumer & Retail Analytics | Econometrics Copyright © 2019 Statswork. All rights reserved

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