Fix markdown lint error

Author: Bluefinger

crates/bevy_entropy/README.md

@@ -156,6 +156,6 @@ The examples provided in this repo demonstrate the two different concepts of par
 
 `rand` provides a number of PRNGs, but under types such as `StdRng` and `SmallRng`. These are **not** intended to be stable/deterministic across different versions of `rand`. `rand` might change the underlying implementations of `StdRng` and `SmallRng` at any point, yielding different output. If the lack of stability is fine, then plugging these into `bevy_entropy` is fine. Else, the recommendation (made by `rand` crate as well) is that if determinism of output and stability of the algorithm used is important, then to use the algorithm crates directly. So instead of using `StdRng`, use `ChaCha12Rng` from the `rand_chacha` crate.
 
-As a whole, which algorithm should be used/selected is dependent on a range of factors. Cryptographically Secure PRNGs (CSPRNGs) produce very hard to predict output (very high quality entropy), but in general are slow. The ChaCha algorithm can be sped up by using versions with less rounds (iterations of the algorithm), but this in turn reduces the quality of the output (making it easier to predict). However, `ChaCha8Rng` is still far stronger than what is feasible to be attacked, and is considerably faster as a source of entropy than the full `ChaCha20Rng`. `rand` uses `ChaCha12Rng` as a balance between security/quality of output and speed for its `StdRng`. CSPRNGs are important for cases when you _really_ don't want your output to be predictable and you need that extra level of assurance, such as doing any cryptography/authentication/security tasks. 
+As a whole, which algorithm should be used/selected is dependent on a range of factors. Cryptographically Secure PRNGs (CSPRNGs) produce very hard to predict output (very high quality entropy), but in general are slow. The ChaCha algorithm can be sped up by using versions with less rounds (iterations of the algorithm), but this in turn reduces the quality of the output (making it easier to predict). However, `ChaCha8Rng` is still far stronger than what is feasible to be attacked, and is considerably faster as a source of entropy than the full `ChaCha20Rng`. `rand` uses `ChaCha12Rng` as a balance between security/quality of output and speed for its `StdRng`. CSPRNGs are important for cases when you _really_ don't want your output to be predictable and you need that extra level of assurance, such as doing any cryptography/authentication/security tasks.
 
 If that extra level of security is not necessary, but there is still need for extra speed while maintaining good enough randomness, other PRNG algorithms exist for this purpose. These algorithms still try to output as high quality entropy as possible, but the level of entropy is not enough for cryptographic purposes. These algorithms should **never be used in situations that demand security**. Algorithms like `WyRand` and `Xoshiro256++` are tuned for maximum throughput, while still possessing _good enough_ entropy for use as a source of randomness for non-security purposes. It still matters that the output is not predictable, but not to the same extent as CSPRNGs are required to be.