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Before you order, simply sign up for a free user account and in seconds you'll be experiencing the best in CFA exam preparation. Quantitative Methods 2 Reading Hypothesis Testing Subject Parametric and Non-Parametric Tests. Seeing is believing!
Find out more. Subject Parametric and Non-Parametric Tests PDF Download In hypothesis tests, analysts are usually concerned with the values of parameters, such as means or variances. To undertake such tests, analysts have had to make assumptions about the distribution of the population underlying the sample from which test statistics are derived. Given either of these qualities, the tests can be described as parametric tests. All hypotheses tests that have been considered in this section are parametric tests.
For example, an F-test relies on two assumptions: Populations 1 and 2 are normally distributed. Two random samples drawn from these populations are independent. The F-test is concerned with the difference between the variance of the two populations. Variance is a parameter of a normal distribution.
Therefore, the F-test is a parametric test. There are other types of hypothesis tests, which may not involve a population parameter or much in the way of assumptions about the population distribution underlying a parameter. Such tests are nonparametric tests. Nonparametric tests have different characteristics: They are concerned with quantities other than parameters of distributions. They can be used when the assumptions of parametric tests do not hold for the particular data under consideration.
They make minimal assumptions about the population from which the sample comes. A common example is the situation in which an underlying population is not normally distributed. Other tests, such as a median test or the sign test, can be used in place of t-tests for means and paired comparisons, respectively. Nonparametric tests are normally used in three cases: When the distribution of the data to be analyzed indicates or suggests that a parametric test is not appropriate. When the data are ordinal or ranked, as parametric tests normally require the data to be interval or ratio.
One might be ranking the performance of investment managers; such rankings do not lend themselves to parametric tests because of their scale. When a test does not involve a parameter. For instance, in evaluating whether or not an investment manager has had a statistically significant record of consecutive successes, a nonparametric runs test might be employed. Another example: if you want to test whether a sample is randomly selected, a nonparametric test should be used. In general, parametric tests are preferred where they are applicable.
They have stricter assumptions that, when met, allow for stronger conclusions. However, nonparametric tests have broader applicability and, while not as precise, do add to your understanding of phenomena, particularly when no parametric tests can be effectively used.
Learning Outcome Statements k. LOS Quiz. Subject marked as complete. Subject marked as incomplete. Subject bookmarked for review later on your dashboard. Bookmark removed from your dashboard. Download study notes in a PDF file immediately. Over 5, practice questions that cover the entire CFA curriculum. Global CFA ranking: Know where you stand at all times vs. Why wait? Everything you need to pass your exam is included. Join now and your account will be upgraded immediately! Click here for details.
Register a user account to print out study notes and all practice questions. My Flashcard:. User Contributed Comments 4 User Comment achu non parametric tests weaker, but require fewer distributional assumptions.
AUAU can anyone gives some example for non-parametric tests jpducros Runs tests which examine the pattern of successive increases or decreases in a random variable and rank correlation tests which examine the relation between a random variable's relative numerical periods are examples of non parametric tests. Shaan23 kaplan meier test. I am using your study notes and I know of at least 5 other friends of mine who used it and passed the exam last Dec.
Keep up your great work! My Own Flashcard No flashcard found. Add a private flashcard for the subject. Runs tests which examine the pattern of successive increases or decreases in a random variable and rank correlation tests which examine the relation between a random variable's relative numerical periods are examples of non parametric tests.
It is often used when the assumptions of the T-test For the exact test, the test statistic, T, is the smaller of the two sums of ranks. First, nonparametric tests are less powerful. For example, it is believed that many natural phenomena are 6normally distributed. One issue being highlighted was that these formal normality tests are very sensitive to the sample size of the variable concerned. Parametric vs. Non-Parametric Statistical Tests If you have a continuous outcome such as BMI, blood pressure, survey score, or gene expression and you want to perform some sort of statistical test, an important consideration is whether you should use the standard parametric tests like t-tests or ANOVA vs. With small samples, the parametric test will yield overly low p-values for nonparametric samples, and vice versa.
There are certain assumptions that need to be met for the tests to be valid. One of these assumptions is that the data follow a defined distribution, hence the name parametric after the parameters in the distribution. Sometimes a transformation can be applied to the data so that the requirements are met. However, this will not always be possible for example, where data is distributed in a j-shape or where 2 groups have different distributions and then alternative methods of analysis must be used. This chapter examines alternative nonparametric methods for testing data and how they work. A control group normal patients, mice, assays etc. It is not uncommon to find that the controls react to the treatment but that this is not a strong reaction, whereas some of the comparison group show a very strong reaction.
Need a hand? All the help you want just a few clicks away. Therefore, several conditions of validity must be met so that the result of a parametric test is reliable.
Before you order, simply sign up for a free user account and in seconds you'll be experiencing the best in CFA exam preparation. Quantitative Methods 2 Reading Hypothesis Testing Subject Parametric and Non-Parametric Tests. Seeing is believing!
Before you order, simply sign up for a free user account and in seconds you'll be experiencing the best in CFA exam preparation. Quantitative Methods 2 Reading Hypothesis Testing Subject Parametric and Non-Parametric Tests.
Let us begin this article with the obvious—in the process of data analysis, always look at the data first. By that we mean investigators look first at the numerical and graphical summaries of the data. Checking out the data first provides an overview of the overall project, gives a clearer understanding of the variables and their values, and shows how the values are distributed. How the data is distributed data distribution is characterized by its center , its spread , and the shape of the data. The center refers to the middle of the value distribution, along with an estimate of the value typical of the data. Are the values close to the center or are they widely spread out, and are there significant outliers? The shape of a distribution is described by its number of peaks and by presence or absence of symmetry.
This book demonstrates that nonparametric statistics can be taught from a parametric point of view. As a result, one can exploit various parametric tools such as the use of the likelihood function, penalized likelihood and score functions to not only derive well-known tests but to also go beyond and make use of Bayesian methods to analyze ranking data. The book bridges the gap between parametric and nonparametric statistics and presents the best practices of the former while enjoying the robustness properties of the latter. This book can be used in a graduate course in nonparametrics, with parts being accessible to senior undergraduates. In addition, the book will be of wide interest to statisticians and researchers in applied fields.
This paper presents a modest attempt to review the existing methodologies for measuring short-run abnormal performance of firms following certain corporate events.
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