neuman keuls post hoc why is it not recommended

lucas.M

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23-01-2025

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The Newman-Keuls post hoc test was once a popular method for comparing means after a significant ANOVA result. However, it's now generally discouraged by statisticians. This article will explain why. Understanding the limitations of the Newman-Keuls test is crucial for choosing the right statistical approach for your research.

The Problem with the Newman-Keuls Method

The Newman-Keuls test suffers from a major flaw: inflated Type I error rate. A Type I error occurs when you reject the null hypothesis (that there's no difference between groups) when it's actually true. In simpler terms, you conclude there's a significant difference when there isn't one.

The Newman-Keuls procedure uses a series of pairwise comparisons with progressively shorter critical difference values. This means that as you move down the ordered means, the critical value decreases, increasing the chance of falsely declaring a significant difference, especially when dealing with many groups. The probability of committing at least one Type I error across all comparisons significantly exceeds the nominal alpha level (typically 0.05) you set.

This inflated Type I error rate is the primary reason why the Newman-Keuls test is no longer recommended. Finding a significant difference becomes easier with this method, leading to potentially spurious results and misinterpretations of data. In essence, it increases the chance of finding "false positives."

Why It Was Used (and Why It's Now Obsolete)

The Newman-Keuls method's popularity stemmed from its intuitive simplicity. The stepwise procedure was relatively easy to understand and implement. Before readily available statistical software, this simplicity was a significant advantage.

However, the advancement of statistical software and a greater understanding of multiple comparisons procedures have revealed the test's critical weakness – its inflated Type I error rate. Modern alternatives offer more robust control over Type I error, making the Newman-Keuls test obsolete.

Better Alternatives: Controlling for Family-Wise Error Rate (FWER)

Statisticians now prefer post hoc tests that better control the family-wise error rate (FWER). FWER is the probability of making at least one Type I error when conducting multiple comparisons. Several methods effectively manage FWER:

• Tukey's Honestly Significant Difference (HSD) test: This is a very popular and robust alternative that provides strong control over FWER. It uses a single critical difference value for all pairwise comparisons, regardless of the means' order. This avoids the inflated Type I error issue of the Newman-Keuls test.

• Bonferroni correction: A simple, conservative method that adjusts the alpha level for each comparison. While effective, it can be overly conservative, leading to a higher chance of missing true differences (Type II error).

• Šídák correction: Similar to the Bonferroni correction but slightly less conservative, offering a better balance between Type I and Type II error control.

• Holm-Bonferroni method: This stepwise procedure is less conservative than the Bonferroni correction, offering improved power while still controlling for FWER.

Choosing the appropriate post hoc test depends on your specific research question and the number of groups being compared. Consult statistical resources or an expert for guidance.

Conclusion: Choose More Robust Methods

While the Newman-Keuls test may seem appealing due to its historical usage and seemingly straightforward application, it's crucial to prioritize statistically sound methodologies. Its inflated Type I error rate makes it unreliable for drawing accurate conclusions from your data. Modern alternatives like Tukey's HSD, Bonferroni, Šídák, and Holm-Bonferroni offer superior control over Type I error and are the preferred choices for post hoc comparisons following an ANOVA. Using these robust methods ensures greater confidence in your research findings. Avoid the Newman-Keuls test; it's time to move on to more statistically sound options.