Statistical Non-Parametric for Single-Sample

Statistics Non-Parametric Methods

Created byTaman Belajar, Bina Nusantara University, Erna Fransisca Angela Sihotang

Last updated 6/2025

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What you'll learn

Students can distinguish between parametric and nonparametric, aware of the appropriate use of both parametric and nonparametric methods
Students learn about the distribution of data, interpret a conclusion from the run test for randomness
Students can finish the data ranking process both with and without tie data.
Students can use the significance value and hypothetical test to draw conclusions about the outcome based on signed test
Students can construct the hypothesis for Wilcoxon rank test in one sample case and read the Wilcoxon table.

Course content

6 sections • 11 lectures • 1h 38m total length

Overview2:13
Explore nonparametric methods for single-sample data, including rank data, one-sample sign tests, and Wilcoxon signed rank tests, with Kolmogorov-Smirnov checks for distribution assumptions.

Testing Data Normality13:16
Learn to assess data distribution and normality with histograms, standard deviation, and the empirical rule, then perform a Kolmogorov-Smirnov one-sample test to decide normality in a department store case.
Test of Randomness and Data Scale10:46
interpret conclusions from a run test for randomness to decide if a data sequence is random. classify data measurement scales and link to nonparametric and parametric tests for single-sample data.
Shape and Empirical Rules7:55
Identify data distributions by shape (left-skewed, symmetric, right-skewed) and apply the empirical rule to estimate data ranges with mu and sigma, including a normal distribution case.
Skewness and Kurtosis9:19
Explore skewness and kurtosis to assess sample normality using z-scores and standard errors, and decide between parametric or non-parametric tests via the Kolmogorov-Smirnov test.
Data and Shape Checking

One Sample – Signed Test (One Sided)11:09
Learn to perform the one sample sign test, one-sided, using signs of xi minus mu0 to assess the population median via the binomial p-value and alpha 0.05.
One Sample – Signed Test (Two Sided)9:07
Explore the one-sample sign test in a two-sided framework: form hypotheses about the population median, compute the S statistic from sign counts, and interpret p-values via the binomial distribution.
Signed Test

One Sample - Wilcoxon Signed Rank Test (One Sided)9:06
Apply the one-sample Wilcoxon signed rank test in a one-sided framework to assess if the population median is less than a hypothesized value, using W and the Wilcoxon table.
One Sample - Wilcoxon Signed Rank Test (Two Sided)8:08
Formulate two-sided hypotheses for the one-sample Wilcoxon signed rank test. Compute W and compare to the Wilcoxon table to infer the population median (107) at 5% significance.
Wilcoxon Test

Requirements

No special requirements are needed

Description

Nonparametric statistics are essential for research in the social, behavioral, and health sciences. Non-parametric statistical methods using single sample are explained in this course. Numerous studies in these fields use data that is categorized using an ordinal or nominal scale. Interval data from these fields sometimes doesn’t have enough parameters to be considered normal. For the analysis of such data, nonparametric statistical tests are helpful instruments. There are ten module topics in this course, including:

Definition of Statistic Non-Parametric
Testing Data Normality
Test of Randomness and Data Scale
Shape and Empirical Rules
Skewness and Kurtosis
Rank data and Rank data with tied values
One sample – Signed test - one sided
One sample – Signed test - two sided
One sample - Wilcoxon signed rank test- one sided
One sample - Wilcoxon signed rank test- two sided

The videos in this course consist of 10-15 minutes of watching to the material in each module video and require 20-30 minutes for each module to be directly practiced with case examples that can be done manually. So, the total duration to complete this course is 200 to 300 minutes. After completing this course, participants are expected to understand the definition of non-parametric statistics, test data normality, understand data scale types, and test data randomness. After completing this course, participants are expected to understand the definition of non-parametric statistics, test data normality, understand data scale types, and test data randomness. Participants are also able to understand the types of data shapes by calculating the skewness of the data, followed by understanding how to sort the data, using the sign test, and then the Wilcoxon test both for one-way side and two-way side. In each module, there are manual tests that can be tried. As for the sign and Wilcoxon tests in this course, they are still applied to single data sets. It is recommended that participants prepare their own notes to maximize understanding.

Who this course is for:

Students who want to learn statistical non-parametric for single-sample

Statistical Non-Parametric for Single-Sample

What you'll learn

Explore related topics

Course content

Overview of the Material1 lecture • 2min

Introduction to Non-Parametric Statistic1 lecture • 11min

Data and Shape Checking4 lectures • 41min

Data Rank1 lecture • 6min

Signed Test2 lectures • 20min

Wilcoxon Test2 lectures • 17min

Requirements

Description

Who this course is for: