<blockquote>
<p>(1) Why they noted like that? I saw all variables changed statistically significant after event</p>
</blockquote>
<p>Statistically significant change might not be meaningful (or in their lingo pronounced). For example, if you have new cancer drug and you run a clinical trial that shows that it has statistically significant and positive effect on length of patients life, but that statistical significant effect turns out to be 2 extra days of living, then even if the effect is statistically significant (meaning you likely discovered actual effect), it is not meaningful or pronounced effect.</p>
<blockquote>
<p>(2) The t-test is used to assess the statistical significance of the difference in means between the treatment and control groups, and to determine whether this difference is likely due to chance or to the intervention. A statistically significant difference indicates that the intervention had a significant impact on the outcome variable, while a non-significant difference suggests that the intervention did not have a significant impact.</p>
</blockquote>
<p>This is actually not completely true. t-test <em>alone</em> only tells you whether the difference between groups is likely due to chance or not, it does not <em>alone</em> tells you whether the effect was causal. To establish causality you need to add something extra on top of the estimates e.g. did you randomized people into control and treatment? If yes randomization, if properly done, can be used as an argument the effect is causal but t-test alone can't establish causality.</p>
<blockquote>
<p>So, why the author dont compare the difference of means of treated group and control group before and after event but they did compared control group with control group before and after and compare treated with treated groups before and after event day.</p>
</blockquote>
<p>When it comes to these sort of decisions it's best to contact authors directly, nobody can see in their heads except them. They probably wanted to see whether there was significant change after planning in both control and treatment.</p>

CC BY-SA 4.0
2023-03-12T12:07:03.193Z
2023-03-12T12:07:03.193Z
1muflon1
<p>When reading a paper of <a href="https://www.sciencedirect.com/science/article/pii/S0195925521000986?casa_token=f3Ez1vcmDAIAAAAA:QD43L8qNzUQOuCMO2Tzdmm9nKj-868d67SLUdsHoGk7Dksm0clMKwMBYMBMSwfeoklG0ih6vmpwd" rel="nofollow noreferrer">Fan, 2021</a>, I found two things that I feel it is strange</p>
<p>They conducted a t-test and they argue the results as below</p>
<p><a href="https://i.sstatic.net/2QG3x.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/2QG3x.png" alt="enter image description here" /></a></p>
<p>Simply speaking, Econ, Soci, Ecol, and Util are four dependent variables.
They noted :</p>
<blockquote>
<p>&quot;Except for resource utilization, the treated group have undergone
pronounced changes&quot;
resource utilization is Util in the Table</p>
</blockquote>
<p>So I have two questions here:
(1) Why they noted like that? I saw all variables changed statistically significant after event</p>
<p>(2) The t-test is used to assess the statistical significance of the difference in means between the treatment and control groups, and to determine whether this difference is likely due to chance or to the intervention. A statistically significant difference indicates that the intervention had a significant impact on the outcome variable, while a non-significant difference suggests that the intervention did not have a significant impact.</p>
<p>So, why the author dont compare the difference of means of treated group and control group before and after event but they did compared control group with control group before and after and compare treated with treated groups before and after event day.</p>

<econometrics>
Confusing in explanation of results of t-test
Phil Nguyen
# Confusing in explanation of results of t-test

(Asked by: username_0 on 2023-03-12)

When reading a paper of [Fan, 2021](https://www.sciencedirect.com/science/article/pii/S0195925521000986?casa_token=f3Ez1vcmDAIAAAAA:QD43L8qNzUQOuCMO2Tzdmm9nKj-868d67SLUdsHoGk7Dksm0clMKwMBYMBMSwfeoklG0ih6vmpwd), I found two things that I feel it is strange

They conducted a t-test and they argue the results as below

[![enter image description here](https://i.sstatic.net/2QG3x.png)](https://i.sstatic.net/2QG3x.png)

Simply speaking, Econ, Soci, Ecol, and Util are four dependent variables.
They noted :

> "Except for resource utilization, the treated group have undergone
> pronounced changes"
> resource utilization is Util in the Table

So I have two questions here:
(1) Why they noted like that? I saw all variables changed statistically significant after event

(2) The t-test is used to assess the statistical significance of the difference in means between the treatment and control groups, and to determine whether this difference is likely due to chance or to the intervention. A statistically significant difference indicates that the intervention had a significant impact on the outcome variable, while a non-significant difference suggests that the intervention did not have a significant impact.

So, why the author dont compare the difference of means of treated group and control group before and after event but they did compared control group with control group before and after and compare treated with treated groups before and after event day.

**Tags:** econometrics

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## 1 answers

### Answer by username_1 on 2023-03-12. Score: 1

> (1) Why they noted like that? I saw all variables changed statistically significant after event

Statistically significant change might not be meaningful (or in their lingo pronounced). For example, if you have new cancer drug and you run a clinical trial that shows that it has statistically significant and positive effect on length of patients life, but that statistical significant effect turns out to be 2 extra days of living, then even if the effect is statistically significant (meaning you likely discovered actual effect), it is not meaningful or pronounced effect.

> (2) The t-test is used to assess the statistical significance of the difference in means between the treatment and control groups, and to determine whether this difference is likely due to chance or to the intervention. A statistically significant difference indicates that the intervention had a significant impact on the outcome variable, while a non-significant difference suggests that the intervention did not have a significant impact.

This is actually not completely true. t-test *alone* only tells you whether the difference between groups is likely due to chance or not, it does not *alone* tells you whether the effect was causal. To establish causality you need to add something extra on top of the estimates e.g. did you randomized people into control and treatment? If yes randomization, if properly done, can be used as an argument the effect is causal but t-test alone can't establish causality.

> So, why the author dont compare the difference of means of treated group and control group before and after event but they did compared control group with control group before and after and compare treated with treated groups before and after event day.

When it comes to these sort of decisions it's best to contact authors directly, nobody can see in their heads except them. They probably wanted to see whether there was significant change after planning in both control and treatment.


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