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- Boston University School of Public Health Introduction The three modules on hypothesis testing presented a number of tests of hypothesis for continuous, dichotomous and discrete outcomes. Tests for continuous outcomes focused on comparing means, while tests for dichotomous and discrete outcomes focused on comparing proportions. All of the tests presented in the modules on hypothesis testing are called parametric tests and are based on certain assumptions. For example, when running tests of hypothesis for means of continuous outcomes, all parametric tests assume that the outcome is approximately normally distributed in the population.Link: http://ntstestresults.org/bok-bank-of-khyber-mto-cash-officers-jobs-nts-result/
- This does not mean that the data in the observed sample follows a normal distribution, but rather that the outcome follows a normal distribution in the full population which is not observed. For many outcomes, investigators are comfortable with the normality assumption i. It also turns out that many statistical tests are robust, which means that they maintain their statistical properties even when assumptions are not entirely met. Tests are robust in the presence of violations of the normality assumption when the sample size is large based on the Central Limit Theorem see page 11 in the module on Probability. When the sample size is small and the distribution of the outcome is not known and cannot be assumed to be approximately normally distributed, then alternative tests called nonparametric tests are appropriate. Learning Objectives After completing this module, the student will be able to: Compare and contrast parametric and nonparametric tests Identify multiple applications where nonparametric approaches are appropriate Perform and interpret the Mann Whitney U Test Perform and interpret the Sign test and Wilcoxon Signed Rank Test Compare and contrast the Sign test and Wilcoxon Signed Rank Test Perform and interpret the Kruskal Wallis test Identify the appropriate nonparametric hypothesis testing procedure based on type of outcome variable and number of samples When to Use a Nonparametric Test Nonparametric tests are sometimes called distribution-free tests because they are based on fewer assumptions e.Link: https://theblondelifestyle.com/2017/12/18/mentally-preparing-tests-college/
- Parametric tests involve specific probability distributions e. The cost of fewer assumptions is that nonparametric tests are generally less powerful than their parametric counterparts i. It can sometimes be difficult to assess whether a continuous outcome follows a normal distribution and, thus, whether a parametric or nonparametric test is appropriate. There are several statistical tests that can be used to assess whether data are likely from a normal distribution. Each test is essentially a goodness of fit test and compares observed data to quantiles of the normal or other specified distribution. The null hypothesis for each test is H0: Data follow a normal distribution versus H1: Data do not follow a normal distribution. If the test is statistically significant e. It should be noted that these tests for normality can be subject to low power.Link: https://finance.yahoo.com/quote/TELL/
- Specifically, the tests may fail to reject H0: Data follow a normal distribution when in fact the data do not follow a normal distribution. Low power is a major issue when the sample size is small - which unfortunately is often when we wish to employ these tests. The most practical approach to assessing normality involves investigating the distributional form of the outcome in the sample using a histogram and to augment that with data from other studies, if available, that may indicate the likely distribution of the outcome in the population. There are some situations when it is clear that the outcome does not follow a normal distribution. These include situations: when the outcome is an ordinal variable or a rank, when there are definite outliers or when the outcome has clear limits of detection. Using an Ordinal Scale Consider a clinical trial where study participants are asked to rate their symptom severity following 6 weeks on the assigned treatment.Link: https://youtube.com/playlist?list=PLTrLnQgHv5x0IfXJpPGF-5fuLnJybJt5R
- Symptom severity might be measured on a 5 point ordinal scale with response options: Symptoms got much worse, slightly worse, no change, slightly improved, or much improved. Distribution of Symptom Severity in Total Sample The distribution of the outcome symptom severity does not appear to be normal as more participants report improvement in symptoms as opposed to worsening of symptoms. When the Outcome is a Rank In some studies, the outcome is a rank. The score, which ranges from , is the sum of five component scores based on the infant's condition at birth. APGAR scores generally do not follow a normal distribution, since most newborns have scores of 7 or higher normal range. When There Are Outliers In some studies, the outcome is continuous but subject to outliers or extreme values. For example, days in the hospital following a particular surgical procedure is an outcome that is often subject to outliers. Suppose in an observational study investigators wish to assess whether there is a difference in the days patients spend in the hospital following liver transplant in for-profit versus nonprofit hospitals.Link: https://knowalot.org/math-kids-quiz.htm
- The number of days in the hospital are summarized by the box-whisker plot below. Recall from page 8 in the module on Summarizing Data that we used Q In the box-whisker plot above, Limits of Detection In some studies, the outcome is a continuous variable that is measured with some imprecision e. For example, some instruments or assays cannot measure presence of specific quantities above or below certain limits. HIV viral load is a measure of the amount of virus in the body and is measured as the amount of virus per a certain volume of blood. It can range from "not detected" or "below the limit of detection" to hundreds of millions of copies. Thus, in a sample some participants may have measures like 1,, or , copies and others are measured as "not detected. Hypothesis Testing with Nonparametric Tests In nonparametric tests, the hypotheses are not about population parameters e. Instead, the null hypothesis is more general. In a nonparametric test the null hypothesis is that the two populations are equal, often this is interpreted as the two populations are equal in terms of their central tendency.Link: https://fixya.com/motorcycles/p985858-yamaha_fzx_750_1993
- Advantages of Nonparametric Tests Nonparametric tests have some distinct advantages. With outcomes such as those described above, nonparametric tests may be the only way to analyze these data. Outcomes that are ordinal, ranked, subject to outliers or measured imprecisely are difficult to analyze with parametric methods without making major assumptions about their distributions as well as decisions about coding some values e. As described here, nonparametric tests can also be relatively simple to conduct.Link: https://en.wikipedia.org/wiki/Z-test
Monday 24 May 2021
Mcq On Non Parametric Test With Answers
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