Using statistics is wonderful and feels ever-so-scientific, but I’m wondering if this particular problem is more easily solved by using what I’d call best professional judgement.
I’m assuming the park is in California or, at least, some other well-studied large region in North America. Currently, you have 800 plant species on your park’s list (just vascular plants?). Calflora says it has 8,000 plants (I assume they mean species). So, the question becomes which of the 7,200 species that aren’t currently on your list species are fairly likely to show up in your park.
Then you assemble a team of botanical experts familiar with your larger region (or just you and graysquirrel). You can use the iNat-based Easily Missed tool and Calflora’s What Grows Here tool to generate lists of what grows nearby/in similar habitats. Then it’s brute force - maybe a day or two of pleasurable perusal - to eliminate species out of the 7,200 that aren’t likely to grow in the park. No ocean in/next to your park? Thus, no marine plants. No true desert/high alpine/lakes/ponds/rivers in your park? No desert/alpine/submerged aquatic plants. You have a rather nice big bog, you say? Pile those bog plants into the more likely category. Some of the 7,200 are waifs or adventive? Not terribly likely to be in your park.
This brute force, er, best professional judgment method has the advantage of acquainting you and graysquirrel (and any others on your botanical team) with the most likely suspects still to be documented and thus giving you clues about where and when to search, not to mention what kinds of experts to invite to help out.
It would be interesting to compare this brute force method with methods based on sampling and statistics, both in terms of how much effort it takes to generate an estimate and how accurate each method proves after five or ten years of field effort.