Hi @Manisha !
Welcome in the Community !
What is your objective (s) with this DoE : identify important effects, optimize your system, test the robustness of your system against noise factors, ... ? What are your factors ? Why are they only categorical with 3 levels ?
There are many possibles explanations to why you may not find any effects statistically significant:
- High noise/experimental variability in response : Have you any replicate runs in your design that may help estimate experimental variability ?
- Factors ranges too small : in your case you're using only categorical factors, but are the levels studied different enough to see some variation ?
- Missing of other important factors in the design
- Inappropriate model and/or factors definition
- Maybe these factors are indeed statistically non-significant and impactful on the response studied and with the levels tested.
- ...
Regarding your design, I guess you have done all combinations involving your 2 three-levels factors (3x3). Maybe you could add some runs to have replicated runs and better estimate experimental variability, or adjust the p-value threshold depending on the power analysis : if you're very early in your study and regarding the small number of experiments, I doubt that a "standard" p-value threshold of 0.05 will enable to identify statistically significant effect. You could use the Power analysis when designing your DoE with an estimate of anticipated noise in your response to adjust the size of the DoE and/or the alpha level.
What are the effects estimates ? Are they practically important ? Do they have a meaningful influence on the response ?
You can also read this similar topic (and responses) for information : Using DOE result as a quantitative or qualitative prediction (based on effect summary)
With more information about your context it will be easier to help you,
