Hypothesis Testing

Hypothesis Tests

This section describes the hypothesis tests available in LabPlot. These tools allow users to perform inferential statistics on their data to determine significance, compare groups, and analyze distributions.

Common Applications

Hypothesis tests are essential in Research, Quality Assurance, and Data Analysis across various fields (e.g., science, engineering, sociology). They help analysts:

  • Determine if an experimental treatment has a real effect (e.g., drug vs. placebo).

  • Compare performance between different groups or manufacturing processes.

  • Analyze survey data to find associations between categorical variables.

  • Assess reliability and survival times in engineering or medical studies.

Available Tests

LabPlot currently supports a comprehensive suite of tests, categorized into parametric and non-parametric methods.

Parametric Tests

Parametric tests assume that the data follows a specific distribution, typically a normal distribution. These tests are generally more powerful than non-parametric tests when their assumptions are met.

  • One-Sample t-Test: Used to compare the mean of a single sample to a known value or theoretical population mean.

  • Independent Two-Sample t-Test: Compares the means of two independent groups to determine if there is a statistically significant difference between them. This test assumes equal variances.

  • Paired Two-Sample t-Test: Compares means from the same group at different times (e.g., before and after an intervention) or matched pairs.

  • Welch’s t-Test: An adaptation of the independent t-test used when the two samples have unequal variances.

  • One-Way ANOVA Test: A parametric test comparing the means of three or more independent groups to determine if at least one group mean is statistically different from the others.

  • One-Way ANOVA with Repeated Measures Test: Used when the same subjects are measured multiple times under different conditions to detect changes over time or across treatments.

Non-Parametric Tests

Non-parametric tests do not assume a normal distribution and are useful for ordinal data, ranked data, or when parametric assumptions are violated.

  • Mann-Whitney U Test: A non-parametric alternative to the independent t-test, used to assess whether two independent samples come from the same distribution.

  • Wilcoxon Signed Rank Test: The non-parametric equivalent of the paired t-test, used to compare two related samples or repeated measurements on a single sample.

  • Kruskal-Wallis Test: An extension of the Mann-Whitney U test for more than two groups; it serves as a non-parametric alternative to One-Way ANOVA for independent samples.

  • Friedman Test: A non-parametric alternative to the One-Way ANOVA with Repeated Measures, used to detect differences in treatments across multiple test attempts.

  • Chi-Square Independence Test: Used to determine if there is a significant association between two categorical variables.

  • Chi-Square Goodness of Fit Test: Used to determine if a sample distribution matches a theoretical population distribution.

  • Log-Rank Test: A hypothesis test to compare the survival distributions of two samples. It is widely used in clinical trials and reliability engineering to analyze time-to-event data.

Usage

Refer to the LabPlot data analysis UI (Analyze / Statistics dialogs) within the application for step-by-step usage of each test. Each test in LabPlot provides options to select input columns, choose grouping variables, and set test-specific parameters. For details and examples, consult the relevant subsections in this documentation (to be added) or the application help dialogs.