Analysis of Variance

ANOVA is used to determine if there are differences in the mean in groups of continuous data. It answers the question...Is the mean of at least one group different than the mean of other (multiple) groups of data? 

The test is used in the ANALYZE phase of a DMAIC project. A GB/BB should be very comfortable using and understanding the mechanics behind this test. It is likely to be one of the most common tests that will be used by a Six Sigma project manager.

ANOVA is a commonly used as a hypothesis test for means (not median or mode) and usually is applied for testing >2 means (use 1-sample t or 2-sample t test for one or two means testing respectively).


  • Each sample is normally distributed.
  • Each sample has equal variances.
  • Each sample is independent. There are no patterns or trends present. The changing of one data point should not change another. 
  • The Y-data is variable type of data (such as time).
  • The X-data is attribute data (such as appraiser name).

ANOVA uses two components of variance and the F test to test the two components:

  • BETWEEN sample variance
  • WITHIN sample variance

BETWEEN sample variance is a study of the variation among all the samples usually due to process difference or factor changes.

WITHIN sample variance explains the variation within each sample itself (look at a Box Plot of one data set to graphically comprehend this - the tip of one whisker to another).

ANOVA answers the question if the means of several populations are statistically different or equal. It also computes a lot of other valuable insight that can help steer a GB/BB in a clearer direction. A statistical difference is found when the difference BETWEEN samples is large enough "relative to the difference WITHIN the samples.

The t-test are limited to comparing up to just two groups. Where as, ANOVA can compare 3 groups, 15 groups, 25 groups, and more. 

ANOVA Jargon

Factor (Process Input Variable - PIV, x): A controlled or uncontrolled variable (independent variable) whose influence is being evaluated.

Factor Level (+1,-1, Hi, Low, + , - , A, B): Factor setting.

Response (Process Output Variable - POV, y): The output of the process.

Inference Space: Range of the factors being evaluated.

Fit: Predicted value of the POV (y) with a specified setting of factors.

Residual: Difference from the fit and actual experimental output.

Hypothesis Testing

The following illustrates how the hypothesis test is written along with comments:

Ho: Mean 1 = Mean 2 = Mean 3 = Mean n   

where n = number of samples or levels or samples

HA: at least 1 Mean is different from the other Means

(read that is possible that only one sample mean is different from the other 3, 50, or 100 sample means. Removing the one sample could completely change the result of the test. That is why visual depiction, such as Box Plots, can help find the drivers to the test result or samples that are flawed). 

If the Null Hypothesis, Ho, is found to be true, then we would not expect to see a lot of variation Between Samples. All the population means are considered equal.

If Ho is not true, expect to see significant variation between the samples. This would imply that the difference between samples is large relative to the variation within samples.

Reminder: Statistical significance does not always imply practical significance. Every numerical result needs to taken under scrutiny to determine if it makes sense in reality.


  1. State the Practical Problem 
  2. Determine the Factor and Levels of Interest
  3. Determine the alpha risk (typically 5%)
  4. Determine the beta risk (typically 10-20%)
  5. Establish the Effect Size (Epsilon E)
  6. Establish the Sample Size and collect samples
  7. Plot the data visually (such as a Box Plot)
  8. Construct the ANOVA table
  9. Calculate the test statistic (F) and p-value
  10. Run the ANOVA hypothesis test for equal means*
  11. Verify assumptions are met (normality) and examine the residuals
  12. If the Ho was rejected, determine which mean(s) are different. Looking at the Box Plot and Confidence Intervals are easy way to pick them out. Fisher's Pair-Wise comparison is another  statistical method.
  13. Calculate Epsilon-squared. This explains the % of variation from a given factor. A low value may indicate that other factors may exist. 
  14. Review statistical  conclusion and state the practical conclusion. State the level(s) that are different if such is determined. 

* If the p-value is less than a, reject Ho and infer HA.  If the p-value is greater than a, fail to reject the Ho

One-Way ANOVA Example

In a completely randomized design (One-way ANOVA) there is only one independent variable (factor or "x") with >2 treatment levels (you could also use this for two levels) also called classifications. The sample sizes do not have to be equal. 

Determine if there is a significant difference of means in two or more appraisers. The results of a mock study where four appraisers were timed to make an inspection decision on a 13 widgets.

All other criteria are equal.

Since TIME is the only factor, this is a One-Factor or One-Way ANOVA. There are four levels that are controlled in the experiment, one being each appraiser.

The first step is to create the test. In general, if the p-value is lower than the alpha-risk then the alternate hypothesis is inferred (reject the null).

Hypothesis Test:

Null Hypothesis: Population means of the different appraisers are equal.
Alternate Hypothesis: One of the means are not the same

There are 51 Degrees of Freedom computed from (13*4) - 1.

Using a One-Way test with an alpha-risk of 0.05, the p-value is well above 0.05 at 0.847 (see results table below).

The F-statistic, and heavily overlapping confidence intervals are also evidence that there is no difference among any pairs or combinations of them.

It is concluded that there is not a statistical difference between any of the appraisers.

What if?

If the p-value was <0.05, then at least one group of data is different than at least one other group. It doesn't conclude which one...only states that at least one of the four is different than the others. 

Example of One-way ANOVA statistical results

ANOVA results also help understand Variation

The low F-statistic of 0.27 says the variation within the appraisers is greater than the variation between them.

 The F-critical value is 2.81 according to the statistical software (not shown above).  

You can use the F-table above to get a close estimate of the F-critical value. One downfall with tables is sometimes you may not get a precise number since not every combination is shown. However, the table can provide a fairly good estimate and at least allow a decision to be very conclusive.

The numerator has 3 degrees of freedom and the denominator has 48 degrees of freedom. Using the table below shows that the F-critical value is going to be between 2.76 and 2.84. And in this case, both values are much higher than the F-calculated value of 0.27 so the conclusion is the same. 

As a Six Sigma project manager it may be worth re-running (depending on cost and time) the trial with a larger sample size and additional appraiser training to reduce the variation within each one. The variation is fairly consistent among each of them so it appears there is a systemic issue.

As a Six Sigma project manager it may be worth re-running (depending on cost and time) the trial with a larger sample size and additional appraiser training to reduce the variation within each one.

The variation is fairly consistent among each of them so it appears there is a systemic issue that is causing nearly similar amounts of variation within each appraiser.

It is possible that one or a few of the widgets are creating the similar spread in the timing for each appraiser. You may examine the timing performance of each widget and run an ANOVA among the 13 widgets and see if one or more stands out. 

Epsilon-squared is the % of variation related to the Factor, which is the Appraiser. This is 4.84 / 291.69 = 0.01659 = 1.7%. This is a low value so it is possible that other Factors exist that are creating the variation. 


Other factors can be added to this type of test and get more complicated but most statistical software programs can run Two-Way and Three-Way ANOVA. Use Two-Way ANOVA when there are two factors.

Two-Way Hypothesis Tests:

Null Hypothesis: There is no difference in the means of the 1st factor
Null Hypothesis: There is no difference in means of the 2nd factor
Null Hypothesis: There is no interaction between the two factors

Alternate Hypothesis: Means are not equal among the levels of the 1st factor
Alternate Hypothesis: Means are not equal among the levels of the 2nd factor
Alternate Hypothesis: There is an interaction between the two factors

When there are 3 or more factors use ANOVA General Linear Model.

One-Way ANOVA Module - Download

This module provides lessons and more detail about One-Way ANOVA. Understanding the basic meaning and applications for this commonly used test is necessary for any level of a Six Sigma Project Manager.

Click here to purchase the One-Way ANOVA module and view others that are available.

Factor limitation

Keep in mind that One-Way ANOVA (and the t-tests) are comparing 1 FACTOR across multiple groups. The t-test compares 1 FACTOR across one or two groups (such as before/after, or two machines, or two operators, or now/past) 

A multivariate analysis is a tool that evaluates differences among 2 or MORE FACTORS and between multiple groups simultaneously. There are Two-Way and Three-Way ANOVA tools as well but again that is limited to 2 & 3 factors respectively.

Factors are differences in things such as, but not limited to, parts produced (cant compare the production of pencils to the production of screws even if they run on similar machines), services delivered, timedifferent operating conditions, and customer requirements. 

Before jumping into a multivariate analysis, use ANOVA to focus on one factor at a time and learn from that analysis first, then use multivariate if something significant is found.

Once the data is collected the ANOVA takes very little time and evaluating the factors in various ways only provides more and more insight as to their relationship. It is always better to have more than enough information, within reason, than not enough, especially when the analysis takes a few minutes. 

Proceed to multivariate analysis

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