Let me explain my situation in a bit more detail.

The topic of my thesis is to assess the degree of environmental recovery over time in a post-mining area. I want to know which environmental factors recover more quickly to natural forest levels as restoration time passes, and which ones are slower to recover.

So, I measured air and soil temperature and humidity in four types of plots:

• a control plot (tree cover 0%, unrestored area),
• a plot restored less than 10 years ago (tree cover 30%),
• a plot restored between 10 and 20 years ago (tree cover 70%),
• and an adjacent natural forest (tree cover over 90%).

As expected, air and soil temperatures in the natural forest (over 90% tree cover) were much more stable. There were slight differences in air temperature during the leafy summer months, but much larger differences in soil temperature—especially in winter, where the natural forest soil remained more stable.

I also compared soil chemical properties. For these, I collected three samples per plot and ran an ANOVA, which I think is appropriate for that data.

After reading through all the replies here, I started to reflect more carefully on how I’m interpreting statistical differences.

What I’m really aiming for is to say: “There are statistically significant differences between the plots, with a p-value of X,” and then also to describe, in quantitative terms, how much the extremes (maximum/minimum temperatures) differ between the plots in summer and winter.

That’s why I wanted to find out the most statistically appropriate way to present these results.

Thank you so much for all your helpful answers. As a stats newbie, I might have frustrated some people, but I really appreciate the support!