I am currently carrying out research on A&E attendance for my surgical speciality pre and post covid. I have collected data from around 3000 attendances, and have been staring at the data for two months now, unsure of how to progress. The aim is to see whether the lockdown had an affect on a) number of injuries b) type of injuries c) alcohol/drug usage contributing to injuries d) any other comparisons e.g. age/gender.

The data involves comparing the same dates (between certain months) pre covid and during the covid lockdown for attendances to A&E. The attendances are separated into groups for age, gender, type of injury, how it was sustained and whether there was alcohol/drug use involved.

My research leads me to believe I am to use a mann-whitney u test/wilcoxon rank sum test as my data sets are unpaired and do not follow normal distribution.

I would appreciate any help with how to progress from here in term of statistical research and finding whether there is any statistical significance.

I have the data sets to hand on a spreadsheet (it is colour co-ordinated and not tiring to look at). As you can probably tell, I am quite new to this and so any contributions are fully and warmly welcomed.

Many thanks in advance to any contributors!

The data involves comparing the same dates (between certain months) pre covid and during the covid lockdown for attendances to A&E. The attendances are separated into groups for age, gender, type of injury, how it was sustained and whether there was alcohol/drug use involved.

My research leads me to believe I am to use a mann-whitney u test/wilcoxon rank sum test as my data sets are unpaired and do not follow normal distribution.

I would appreciate any help with how to progress from here in term of statistical research and finding whether there is any statistical significance.

I have the data sets to hand on a spreadsheet (it is colour co-ordinated and not tiring to look at). As you can probably tell, I am quite new to this and so any contributions are fully and warmly welcomed.

Many thanks in advance to any contributors!

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