I have a set of data resulting from my research work. I want to know the significant difference in the data values using ANOVA. I have read a lot of literature similar to my work and it shows that most of the researchers have used different superscript letters a, b, c and d along with the data. It is written "the values with different superscript letters in a column are significantly different (p
Please email to me your data so I can help you analyze the data. My email [email protected]
Craig Refugio
The superscripts are used to indicate pairwise comparisons and whether such comparisons are statistically different. This is a standard way of presenting data yielded from ANOVA results.
Hello,
ANOVA is a useful took for comparing inter class significant difference (here class can be multiple columns of data). I have extracted following information from a general web search but it tells you the gist.
To determine whether any of the differences between the means of the different columns/classes are statistically significant, we can compare the p-value to assess the null hypothesis. The null hypothesis states that the population means are all equal (mean of one class or column). Usually, a significance level (denoted as α or alpha) of 0.05 works well. A significance level of 0.05 indicates a 5% risk of concluding that a difference exists when there is no actual difference.
P-value ≤ α: The differences between some of the means are statistically significant
If the p-value is less than or equal to the significance level, you reject the null hypothesis and conclude that not all of population means are equal. Use your specialized knowledge to determine whether the differences are practically significant.
P-value > α: The differences between the means are not statistically significant
If the p-value is greater than the significance level, you do not have enough evidence to reject the null hypothesis that the population means are all equal. Verify that your test has enough power to detect a difference that is practically significant.
You can measure the P value of an ANOVA test easilt using simple platforms like MS Excel, Origin, MATLAB etc. Just google 'ANOVA in MS Excel' and you should be able to find lots of help.
Hope this answer helps you understanding the basic of ANOVA test.
Also, when you see a superscript a,b,c etc. it usually mean for that particular data, the authors have measured P value and found statistical difference. You can use any superscript, there is not specific rules unless specified by the journal or conference template.
I need to know first on how many independent groups or treatments you have in the study. If 3 then you will have u1; u2; and u3. You will form then the null hyoothesis ho and alternatibe hypothwsis ha. Your null Ho: u1=u2=u3 and Ha: u1; u2 and u3 are not equal. Set level of significance alpha @ 0.05 as tge common value. However some researchers set this as 0.01
Then generate ANOVA THROUGH ANY SOFTWARE. WITH THE software output you can see p value (p). Once you see it (p) compare it to alpha. If p value is less than or equal to 0.05 accept ha and conclude there is sufficient evidence to claim that the means among the 3 treatments are different. In this case you need to undergo post hoc analysis to determine which pair of treatments are different.
If p value is greater than 0.05 then conclude that there is sufficient evidence to claim that all of the 3 treatment means are the same or equal to each other.
For post hoc, you can do it by using a software.
You may email me ur data so I can exactly help you in the analysis.
Thanks
Craig Refugio
Please email to me your data so I can help you analyze the data. My email [email protected]
Craig Refugio
Hi Ruchir,
Here's a book link that provides some insight and guidance regarding ANOVA:
https://books.google.com/books?id=lZ08AAAAQBAJ&pg=PR11&dq=ANOVA+%22statistics+for+engineers%22&hl=en&sa=X&ved=0ahUKEwiZlOGAqa_bAhWPylMKHVUNDB0QuwUILjAA#v=onepage&q=ANOVA%20%22statistics%20for%20engineers%22&f=false
Have a great day!
--Adrian
- Badges
- Science method
- Similar topics
- Mathematics
- Statistics
- Statistical Modeling
- Analyses of Variance
- ANOVA