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this post was submitted on 12 Sep 2023
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Your null hypothesis is the thing you're trying to disprove. For example, if I wanted to run a study to asses the effect of adding a certain growth hormone to a cell culture, my null hypothesis would be "there is no effect". In your case, it would be "there is no difference in how much different things are liked". From there, you'd run your study, and do your statistical analysis, for which there are different methods based on the type of data, number of groups your comparing, sample size, etc., and I'm not a statistician so I can't say which methods are best for what you're planning.
When it comes to p-value, to really simplify it, you can think of your p-value as the likelihood your null hypothesis is true. That's not exactly what it means, but it's an easy way to remember it.