<p>You have a <code>2x2</code> table, but you are ignoring one of the rows. You need to add how many non-variants you had in each group. The answer to your question depends on that. Say you had 7 variants out of 30 cases and 0 variants out of 20 controls. Your contingency table would look like this:</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th style="text-align: right;"></th>
<th style="text-align: center;">Cases</th>
<th style="text-align: left;">Controls</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: right;">Variants</td>
<td style="text-align: center;">7</td>
<td style="text-align: left;">0</td>
</tr>
<tr>
<td style="text-align: right;">Non-variants</td>
<td style="text-align: center;">23</td>
<td style="text-align: left;">20</td>
</tr>
</tbody>
</table>
</div>
<p>Without this information, testing makes no sense. Now, you can use an exact test, such as Fisher's (conditional) or Barnard's (unconditional). I have described an unconditional test that seems to be more powerful than those (<a href="https://arxiv.org/abs/2110.14315" rel="nofollow noreferrer">link</a>). In this case,</p>
<pre><code>&gt; library(mtest)
&gt; m.test(list(c(7, 23), c(0, 20)))
[1] 0.01702871
</code></pre>
Is there suitable statistics test to compare number of observations from 2 groups (1x2, Fishers?)?
<p>I did a research on group of cases and controls. During my research I observed <strong>7  variants</strong> in group of cases while I <strong>did not observe</strong> any in controls.</p>
<p>I would like to test is there a <strong>significant difference between 7:0 finding</strong>.</p>
<p>I thought of doing Fishers exact test, but not sure how it can be performed on 1x2 table and is it a suitable test for such analysis. Also, I thought of correction, to exclude 0 from statistics, so maybe to compare 7.5 and <strong>0.5</strong>.</p>
<p>Is there <strong>a better test to perform</strong> for such a cases?</p>
<p>All suggestions are welcomed. Thank you.</p>