In my experience, people often tend to advocate for whatever package they use most often (and not everyone has direct experience with a wide variety of statistical software).
Both packages have a number of virtues, as well as lack a number of features. The "best" package will vary depending on the type of analysis required.
However, I think you've omitted the most extensive and fastest-growing (in features and analytic methods) package in the world: R (https://www.r-project.org/).
In my experience, people often tend to advocate for whatever package they use most often (and not everyone has direct experience with a wide variety of statistical software).
Both packages have a number of virtues, as well as lack a number of features. The "best" package will vary depending on the type of analysis required.
However, I think you've omitted the most extensive and fastest-growing (in features and analytic methods) package in the world: R (https://www.r-project.org/).
No simple answer for your question, as the colleagues said it depends on what tests and hypothesis you are going to test! Yet I preferer the most powerful and open sources with unlimited and frequently updated features "R".
Here is a useful page that briefly reviews the pros and cons of STATA (which I use most often), R (which I use, but less so), and SPSS (I do not use this at all, comparable to Excel in functionality): http://publish.illinois.edu/commonsknowledge/2019/12/05/stata-vs-r-vs-spss-for-data-analysis/
In my opinion, I think it’s just about the interest of the user. However, I am unsure about the strength of SPSS in certain tests. For example, I found Stata quite interesting for socioeconomic inequality analyses, multilevel analysis, survival analysis and so forth.
In my opinion SPSS is too much user friendly so easier for not statistician researcher, but limitating for a statiscian. Personally I suggest to use R to researcher that realy know the statistics