<p>You can interpret insignificant results as not being able to reject null. That would be the standard practice.</p>
<p>However, it’s worrying that you say in some of your model F-Statistics becomes in-estimable. That should not happen just because there aren’t statistically significant relationships. Even when model cannot explain virtually any variation in the data you should be able to have F-Statistics.</p>
<p>Hence you should investigate that problem further. Do you have insufficient amount of observations? If so you can’t even interpret it as not being able to reject null.</p>

CC BY-SA 4.0
2024-02-03T23:40:33.117Z
Thank you very much for your comment! My F-stat is first insignificant and then even drops out because of the quantity of observations. Essentially, my independent variable is a percentage point difference in prices from my experiment. Analyzing the behavior with changes of 10, I have 652 observations. Analyzing changes of at least 15, I only have 342 observations. Another issue is that I cluster on the individual level, so in the first regression I have 380 clusters (i.e., 380 people who even saw changes of 10), while in the regression with 15, I have 253 clusters.
hunter_gatherer
CC BY-SA 4.0
2024-02-03T23:45:18.783Z
Clustering the standard errors on the individual level is necessary to avoid an inflation of the individual t-stats as the observations are not truly independent from each other. Please don't say that I need more observations with changes of 10 and 15 as the data is already collected and I cannot get more data. In the paper, I am hoping to say despite the overall model's insignificance as denoted by the F-stat, I am not interested in collectively examining that all coefficients are equal to zero. I am interested in the individual coefficients in that they are not significant for the dep var
hunter_gatherer
CC BY-SA 4.0
2024-02-03T23:26:35.313Z
2024-02-03T23:26:35.313Z
1muflon1
<p>I have a regression with an insignificant F-stat. In an even stricter regression (with fewer observations), the F-stat becomes inestimable (denoted by Stata with dots in F and Prob &gt; F).</p>
<p>My question relates to whether I can interpret the individual coefficients, which are insignificant in both regressions: As in &quot;none of the independent variables are statistically significant to the dependent variable.&quot;</p>
<p>The null results would greatly help my paper.</p>
<p>Thank you for any advice!</p>

<regression><f-test>
Can you trust a regression model that has an insignificant F-test and insignificant coefficients?
hunter_gatherer
# Can you trust a regression model that has an insignificant F-test and insignificant coefficients?

(Asked by: username_0 on 2024-02-03)

I have a regression with an insignificant F-stat. In an even stricter regression (with fewer observations), the F-stat becomes inestimable (denoted by Stata with dots in F and Prob > F).

My question relates to whether I can interpret the individual coefficients, which are insignificant in both regressions: As in "none of the independent variables are statistically significant to the dependent variable."

The null results would greatly help my paper.

Thank you for any advice!

**Tags:** regression, f-test

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## 1 answers

### Answer by username_1 on 2024-02-03. Score: 1 | Accepted answer

You can interpret insignificant results as not being able to reject null. That would be the standard practice.

However, it’s worrying that you say in some of your model F-Statistics becomes in-estimable. That should not happen just because there aren’t statistically significant relationships. Even when model cannot explain virtually any variation in the data you should be able to have F-Statistics.

Hence you should investigate that problem further. Do you have insufficient amount of observations? If so you can’t even interpret it as not being able to reject null.

> **Comments:**
> **username_0**: Thank you very much for your comment! My F-stat is first insignificant and then even drops out because of the quantity of observations. Essentially, my independent variable is a percentage point difference in prices from my experiment. Analyzing the behavior with changes of 10, I have 652 observations. Analyzing changes of at least 15, I only have 342 observations. Another issue is that I cluster on the individual level, so in the first regression I have 380 clusters (i.e., 380 people who even saw changes of 10), while in the regression with 15, I have 253 clusters.  
> **username_0**: Clustering the standard errors on the individual level is necessary to avoid an inflation of the individual t-stats as the observations are not truly independent from each other. Please don't say that I need more observations with changes of 10 and 15 as the data is already collected and I cannot get more data. In the paper, I am hoping to say despite the overall model's insignificance as denoted by the F-stat, I am not interested in collectively examining that all coefficients are equal to zero. I am interested in the individual coefficients in that they are not significant for the dep var  

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