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**Non Parametric Tests**

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**Parametric** versus Nonparametric Statistics – When to use them and which is more powerful? Angela Hebel Department of Natural Sciences University of Maryland Eastern Shore

Definition of **Non**-**Parametric** Statistics **Non**-**parametric** statistics are a branch of statistics that are applied when populations are not normal, or there are severely skewed data.

Results May Be as Exact as **Parametric** Procedures Disadvantages of Nonparametric **Tests** 1. May Waste Information **Parametric** model more efficient if data Permit 2. Difficult to Compute by hand for Large Samples 3. Tables ...

Chapter 9: **Non**-**parametric** **Tests** **Parametric** vs **Non**-**parametric** Chi-Square 1 way 2 way **Parametric** **Tests** Data approximately normally distributed. Dependent variables at interval level.

Nonparametric Statistics Timothy C. Bates [email protected] **Parametric** Statistics 1 Assume data are drawn from samples with a certain distribution (usually normal) Compute the likelihood that groups are related/unrelated or same/different given that underlying model t-test, Pearson’s ...

**Non**-**Parametric** **Tests** Wilcoxon Rank Sum Test Used to compare two independent samples Equivalent to Mann-Whitney U test. Like the Signed-rank test, the Rank-Sum test is based on the ranks of the data.

**Parametric** or **Non**-**Parametric** **Tests**. Choosing the right test to compare measurements is a bit tricky, as you must choose between two families of **tests**:

**Non**-**Parametric** Statistics A Presentation by Rob McMullen for AP Statistics What are **Non**-**Parametric** Statistics? **Non**-**parametric** statistics are a special form of statistics which help statisticians with a problem occuring in **Parametric** statistics.

Nonparametric: **Parametric** **Parametric** Dependent Variable: Interval/Ratio **Parametric** v. Nonparamteric: ... Nonparametric **Tests**,2 Independent Samples Move DV to Test Variable list Move IV to Grouping Variable Define Groups Make sure M-W is checked Interpreting the Printout Mean ranks z-value ...

**Non**-**parametric** equivalents to the t-test Sam Cromie **Parametric** assumptions Normal distribution (Kolmogorov-Smirnov test) For between groups designs homogeneity of variance (Levene’s test) Data must be of interval quality or above Scales of measurement - NOIR Interval adjacent data points are ...

Categorical and discrete data. **Non**-**parametric** **tests** **Non**-**parametric** **tests**: estimate sample differences when the known distribution shapes cannot help, or even confuse Metrics of arbitrary distibutions Median: the value that "splits the sample in half" Mode: the value that occurs with the greatest ...

**Parametric** **Tests** 1) Assumption of population normality 2) homogeneity of variance **Parametric** more powerful than nonparametric Nonparametric **Tests** Nonparametric Test (distribution free testing) 1) Pathological conditions are represented by skewed distributions 2) Small clinical samples and ...

Statistics Idiots Guide! Dr. Hamda Qotba, B.Med.Sc, M.D, ABCM Definition **Parametric** Vs. **non** **parametric** **tests** **Parametric**: decision making method where the distribution of the sampling statistic is known **Non**-**Parametric**: decision making method which does not ...

Chapter 18: The Chi-Square Statistic **Parametric** and Nonparametric **Tests** Chapter 18 introduces two **non**-**parametric** hypothesis **tests** using the chi-square statistic: the chi-square test for goodness of fit and the chi-square test for independence.

**Non**-**parametric** **Tests** e.g., Chi-Square When to use various statistics **Parametric** Interval or ratio data Name **parametric** **tests** we covered Tuesday **Non**-**parametric** Ordinal and nominal data Chi-Square X2 Chi Square **tests** the difference in frequency distributions of two or more groups.

Statistical **Tests** The Friedman Test is like the Sign test, (compares the means of “paired”, **non**-**parametric** samples) for more than two samples.

Title: Lesson 12 **Non**-**parametric** Inference Author: Ken Portier Last modified by: junggran Created Date: 4/2/2000 10:36:37 PM Document presentation format

Ch 9 **Non**-**parametric** **tests** Mainly interested in ranking (distribution) Normality of data may be violated. Sign test, rank sum test, signed-rank test, Kruskal-Wallis test Summary Ch 10 Categorical Data Analysis Learning Objectives Comparison of binomial proportion using Z and 2 Test.

Numeracy & Quantitative Methods: Level 7 – Advanced Quantitative Analysis Test used will depend on data type (nominal, ordinal, scale) **Tests** classified as: **Parametric** (scale and normally distributed data) **Non** **parametric** (nominal/ordinal and/or break assumptions of normal distribution) **Non** ...

**Parametric** vs. **Non**-**parametric**. **Parametric** **Tests** – Statistical **tests** that involve assumptions about or estimations of population parameters. (what we’ve been learning)

Statistical Methods II Session 8 **Non** **Parametric** Testing – The Wilcoxon Signed Rank Test In the previous session, we introduced the concept of **Non**-**Parametric** **tests** (your Plan B **tests**).

Chapter 17 Chi-Square and other Nonparametric **Tests** James A. Van Slyke Azusa Pacific University Distinctions between **Parametric** and Nonparametric **Tests** **Parametric** **tests** (e. g. t, z) depend substantially on population characteristics Nonparametric **tests** depend minimally on population parameters ...

Today’s Lecture Topic **Non** **Parametric** vs **Parametric** **Tests** Pros and Cons Statistical Power Wilcoxon Rank Sum Test (AKA Mann-Whitney U-Test) Small Sample Size – Tables

**Non**-**parametric** **tests**. Used when: - assumptions on the distributions of the data are clearly not valid; - a small fraction of the data are considered outliers (either tail), but are not removed as they are experimentally valid (related to first reason above actually)

... can’t tell which sample is different. Would need to do pair-wise **tests**. **Non**-**parametric** **Tests** Testing proportions (Pearson’s) Chi-Squared ( 2) ...

**Non**-**parametric** Approaches The Bootstrap * * * **Non**-**parametric**? **Non**-**parametric** or distribution-free **tests** have more lax and/or different assumptions Properties: No assumption about the underlying distribution being normal More sensitive to medians than means (which is good if you’re interested ...

**Non** – **Parametric** Test Dr.L.Jeyaseelan Dept. of Biostatistics Christian Medical College Vellore, India Introduction No rigid assumptions about the distribution of the populations - “Distribution-free **tests**” Answers the same sort of questions as the **parametric** test – for each **Parametric** ...

Biostatistics in Research Practice: **Non**-**parametric** **tests** Dr Victoria Allgar What is a **non**-**parametric** test ? Methods of analysis that do not assume a particular family of distributions for the data.

STAT 3120 Statistical Methods I Lecture 10 **Non** **Parametric** Testing STAT3120 – **Non** **Parametric** Previously, we discussed the three types of ttests, which represented the most common applications of hypothesis testing: One Sample ttest – uses the mean of a single sample to determine if the ...

**PARAMETRIC** VERSUS NONPARAMETRIC STATISTICS Heibatollah Baghi, and Mastee Badii **Parametric** Assumptions **Parametric** Statistics involve hypothesis about population parameters (e.g., µ, ρ). They require assumptions about the population distribution.

**Non**-**Parametric** Statistics. Most are based on the chi-square statistic and are used to look at the relationship between two ordinal or nominal variables, allowing us to control for:

**Non**-**parametric** **tests** lack parameters Rank **tests** start by ranking the data Distribution-free **tests** don’t assume a Normal distribution (or any other) Lecture Outline What is a nonparametric test? Rank **tests**, ...

Test statistic: Summary (t-**tests**) **Non**-**Parametric** No distribution Paired vs. Unpaired Types: Wilcoxon Mann-Whitney Rank Sum Test Wilcoxon signed rank test Wilcoxon Mann-Whitney Rank Sum Test T-statistic applied to the ranks, not data Intended for not-normal ...

**Non**-**parametric** **tests** Note: When valid, use **parametric** Commonly used Wilcoxon signed-rank test Wilcoxon rank-sum test Spearman rank correlation Chi square etc. Useful for **non**-normal data If possible use some transformation If normalization not possible Note: CI interval ...

**Parametric** and **non**-**parametric** **tests** Norm-referenced, criterion-referenced and domain-referenced **tests** Commercially produced **tests** and researcher-produced **tests** Constructing a test Software for preparation of a test Devising a pre-test and post-test Ethical issues in testing Computerized ...

HBI: Kruskal-Wallis Test Excel: N/A Differences Between Means – **Non**-**Parametric** Data Chi square **tests** compare observed frequency distributions, either to theoretical expectations or to other observed frequency distributions.

The term "**non**-**parametric**" refers to the fact that the chi‑square **tests** do not. 1. require assumptions about population parameters, or . 2. test hypotheses about population parameters.

**Non**-**parametric** **tests** Note: When valid use **parametric** Commonly used Wilcoxon Chi square etc. Performance comparable to **parametric** Useful for **non**-normal data

X2 as a **Non**-**parametric** Test As a **Non**-**parametric** test, Chi-square can be used (i) as a test of goodness of fit and ... **Parametric** **Tests** The Z or t-test is used to determine the statistical significance between a sample statistic and a population parameter.

Three **non**-**parametric** **tests** for continuous variables. Binomial **tests**, McNemar’s test and CMH test for categrical variables. Confidence interval and odds ratio. Thank You For more information, ...

**NON**-**PARAMETRIC** DATA * At the end of the experimental treatment period, the plants are evaluated in a number of ways in order to determine the effect of the treatments on fungal growth.

The t-test, the paired t-test, and introduction to **non**-**parametric** **tests** July 8, 2004 The t-test: for comparing means (averages) Comparing two means Is the difference in means that we observe between two groups more than we’d expect to see based on chance alone?

More on **Parametric** and Nonparametric Population Modeling: a brief Summary Roger Jelliffe, M.D. USC Lab of Applied Pharmacokinetics See also Clin PK, Bustad A, Terziivanov D, Leary R, Port R, Schumitzky A, and Jelliffe R: **Parametric** and Nonparametric Population Methods: Their Comparative ...

Assumptions for **Parametric** **Tests** Dependent variable is a scale variable interval or ratio If the dependent variable is ordinal or nominal, it is a **non**-**parametric** test Participants are randomly selected If there is no randomization, ...

Use R! – do this for HW ( and apply to #15 on page 74) There are also **non**-**parametric** **tests** which can be done to compare scale parameters... FIGURE 2.8.1, page 52 – two distributions with the same location parameters, but different scale parameters. * *

They tend to use less information than the **parametric** **tests**. 3. They are less efficient than their **parametric** counterparts when the assumptions of the **parametric** methods are met.

the sign test is one of the simplest **non**-**parametric** **tests** to apply, but is not very efficient see page 127 for a discussion of the relative efficiencies of some of the paired **tests** we’ve been considering in Chapter 4.

The paired t-test, **non**-**parametric** **tests**, and ANOVA July 13, 2004 Review: the Experiment (note: exact numbers have been altered) Grade 3 at Oak School were given an IQ test at the beginning of the academic year (n=90).

**Parametric** vs. **Non**-**parametric** Nonparametric **Tests** require nominal or ordinal level data Samples complied form different populations and we want to compare the distribution of a single variable within each of them Variables are nominal or can only be rank ordered Very small samples: ...

Nonparametric Inference Why Nonparametric **Tests**? We have been primarily discussing **parametric** **tests**; i.e. , **tests** that hold certain assumptions about when they are valid, e.g. t-**tests** and ANOVA both had assumptions regarding the shape of the distribution (normality) and about the necessity of ...