Is the difference statistically significant?
But statistical significance is not the same as practical significance. Often times, when differences are small but statistically significant, it is due to a very large sample size; in a sample of a smaller size, the differences would not be enough to be statistically significant.
What does P-value signify?
In statistics, the p-value is the probability of obtaining results at least as extreme as the observed results of a statistical hypothesis test, assuming that the null hypothesis is correct. A smaller p-value means that there is stronger evidence in favor of the alternative hypothesis.
What is a statistically significant p value?
A p-value less than 0.05 (typically ≤ 0.05) is statistically significant. A p-value higher than 0.05 (> 0.05) is not statistically significant and indicates strong evidence for the null hypothesis. This means we retain the null hypothesis and reject the alternative hypothesis.
How do you explain significant difference?
A Significant Difference between two groups or two points in time means that there is a measurable difference between the groups and that, statistically, the probability of obtaining that difference by chance is very small (usually less than 5%).
What should statistical test for cluster analysis results should?
HELP. DEB BTW As Dr Etuk points out if the cluster analysis algorithm makes sense and was properly applied the clusters should be different. Now you might want to consider the outliers depending on your goal in doing the analysis. Dr. Ette Etuk Thank you for your feedback and clarification.
How can I identify clusters in my data?
5 Techniques to Identify Clusters In Your Data 1 Cross-Tab. Cross-tabbing is the process of examining more than one variable in the same table or chart (“crossing” them). 2 Cluster Analysis. Cluster analysis groups related items together using different algorithms to identify the “clusters.” 3 Factor Analysis.
What does consensus on number of clusters mean?
A consensus on a number of clusters is an indication of strong differentiation in the dataset. You could also look at plotting principal components and look at the separation. Thanks for contributing an answer to Cross Validated!
What happens if the clusters have different variances?
Fourth If the clusters have different variances, the cluster distributions must be different except for the presence of those outliers which again muddy the water. It looks to me like you have used a lot of computer time without a plan to achieve your goal which you have not mentioned.