Update README.md

Author: bitfaster

README.md

@@ -77,9 +77,12 @@ MemoryCache is perfectly servicable. But in some situations, it can be a bottlen
 
 ## Lru Hit rate
 
-Analysis of 1 million samples of a Zipfan distribution with different *s* values. There are 50,000 total keys, and the test was run with the cache configured to different sizes expressed as a percentage of the total key space.
+The charts below show the relative hit rate of classic LRU vs Concurrent LRU on a [Zipfian distribution](https://en.wikipedia.org/wiki/Zipf%27s_law) of input keys, with parameter *s* = 0.5 and *s* = 0.86 respectively. If there are *N* items, the probability of accessing an item numbered *i* or less is (*i* / *N*)^*s*. 
 
-When the cache is small, below 15% of the total key space, ConcurrentLru significantly outperforms ClassicLru.
+Here *N* = 50000, and we take 1 million sample keys. The hit rate is the number of times we get a cache hit divided by 1 million.
+This test was repeated with the cache configured to different sizes expressed as a percentage *N* (e.g. 10% would be a cache with a capacity 5000).
+
+When the cache is small, below 15% of the total key space, ConcurrentLru outperforms ClassicLru.
 
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@@ -94,6 +97,8 @@ When the cache is small, below 15% of the total key space, ConcurrentLru signifi
 
 ## Lru Benchmarks
 
+In the benchmarks, a cache miss is essentially free. These tests exist purely to compare the raw execution speed of the cache code. In a real setting, where a cache miss is presumably quite expensive, the relative overhead of the cache will be very small.
+
 Benchmarks are based on BenchmarkDotNet, so are single threaded. The ConcurrentLru family of classes can outperform ClassicLru in multithreaded workloads.
 
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@@ -108,7 +113,7 @@ Job=RyuJitX64  Jit=RyuJit  Platform=X64
 
 ### Lookup keys with a Zipf distribution
 
-Take 1000 samples of a [Zipfan distribution](https://en.wikipedia.org/wiki/Zipf%27s_law) over a set of keys of size *N* and use the keys to lookup values in the cache. If there are *N* items, the probability of accessing an item numbered *i* or less is (*i* / *N*)^*s*. 
+Take 1000 samples of a [Zipfian distribution](https://en.wikipedia.org/wiki/Zipf%27s_law) over a set of keys of size *N* and use the keys to lookup values in the cache. If there are *N* items, the probability of accessing an item numbered *i* or less is (*i* / *N*)^*s*. 
 
 *s* = 0.86 (yields approx 80/20 distribution)<br>
 *N* = 500