<h3>Trim gratuitous content in questions (and make the question clear)</h3>
<p>Questions on CV.SE may contain unnecessary content that distracts from the substance of the question.  This excess content can make it harder to see the question at issue.  Common forms of gratuitous content include preliminary apologies for lack of understanding, thanks for attention to the question, or pleas of urgency (hat-tip to <a href="https://stats.meta.stackexchange.com/users/22047/nick-cox">Nick Cox</a> for pointing this out in comments).  The <a href="https://meta.stackexchange.com/tour">description of StackExchange</a> clearly states that &quot;This site is all about getting answers.  It's not a discussion forum.  There's no chit-chat.&quot;</p>
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<li><p><strong>Remove apologies and thanks:</strong> If the question contains any preliminary apology or self-effacement for lack of understanding, or thanks-in-advance for attention or answers, these should generally be removed.  It is not necessary for users to apologise for lack of understanding of statistical material.</p>
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<li><p><strong>Remove pleas of urgency or attention:</strong> Not only is this content gratuitous, but it also solicits special attention to a question.  Such content does not advance the goal of giving a clear question or constructing long-term value for the site.</p>
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<li><p><strong>Remove gratuitous context:</strong> Sometimes context is useful to understand the substance of a question, but in some cases it is gratuitous.  Care should be taken to distinguish these cases, but if gratuitous context is added, this can often be trimmed or removed.</p>
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<li><p><strong>Make the question clear and prominent:</strong> In some cases the question at issue is hidden among other material and it is difficult for the reader to find.  If the question is buried inside large amounts of contextual content, or if the question is only implicit, it can take multiple readings of the question to understand what is being asked.  In such cases it is useful to make the question more prominent and make it explicit.  This can be done by explicitly marking the question at the end of the required context (an example of this technique is shown <a href="https://stats.stackexchange.com/questions/503814/">here</a>).</p>
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<li><p><strong>Don't change the substance of the question, or remove a preliminary confusion:</strong> When trimming gratuitous content from questions, it is important not to change the substance of the question.  This includes ensuring that you do not remove preliminary assertions that are confused or false, since those parts constitute areas where the questioner may need a response that corrects their preliminary misunderstandings.  Care should be taken to determine when gratuitous material can be trimmed without altering the substance of the question.  If in doubt, ping the OP to draw attention to your changes and ask if this still captures the essence of their question.</p>
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<p><strong>Bad version:</strong> Hi everyone.  Sorry if this post doesnt make sense but I'm not a statistics expert and I only did a few simple courses many years ago.  I really need help on this for a project and my boss is putting pressure on me to get it done, so I really need an answer quickly!  I'm doing analysis where x and y are whole numbers and I did linear regression.  All good so far and I got results, but a collegue said you can't use linear regression for whole numbers.  I need to figure out if there is a relationship so then I used correlation.  Correlation is 0.3341 so it confirmed a relationship.  Collegue is still not happy with it and says correlation is also not good for whole numbers.  Ive looked at lots of other posts on regression and I can't figure out what to do when my variables are correlated by they are whole numbers also.  My x is number of days of TV advertising for companies and my y is number of offices for company.  I want to look at relationship with sales revenue maybe and I have another variable for this.  Maybe I need to add this in too but first I am doing this other part. Thanks in advance for helping me!  If I can know how these things affect sales revenue I will make my boss happy!  I'm going to do more statistics courses later to learn this but at the moment I don't know it well, so thanks for attention.</p>
<p><strong>Good version:</strong> I am examining advertising and sales for companies.  I have a dataset containing data for companies, with variables for the sales revenue, the number of offices, and the number of days of TV advertising.  I want to know the effect of advertising days on sales revenue.  So far I have fit a linear regression between number of offices and advertising days and I have also computed the correlation between these variables.  However, I have been told that linear regression and correlation are not suitable for whole number variables.  What should I do to determine the effect of the other two variables on sale revenue?</p>
