diff --git a/README.rst b/README.rst
index f398b48..40abbef 100644
--- a/README.rst
+++ b/README.rst
@@ -284,13 +284,13 @@ The default is to create a phase-neutral (aka linear-phase)
 impulse response. It's also possible to create a minimum-phase response.
 Even everything in between is possible. This is done with the **Causality**
 parameter, where 0% is phase-neutral and 100% is minimum-phase.
-Varying the causality smoothly transitions between the extremes:
+By varying the causality a smooth transition between the extremes is made:
 
 .. image:: images/causality-waveform.png
 
 The latency decreases proportional to the causality.
 At 100% the response becomes zero-latency and purely causal, where only the current
-and later sound samples are used. The pre-echo is eliminated at the price of
+and past sound samples are used. The pre-echo is eliminated at the price of
 twice the post-echo.
 Note that despite the name of "minimum-phase"
 this setting actually incurs the most phase distortion, which can get severe with
diff --git a/hifiscan/analyzer.py b/hifiscan/analyzer.py
index 7f90b42..a7b21d4 100644
--- a/hifiscan/analyzer.py
+++ b/hifiscan/analyzer.py
@@ -153,7 +153,8 @@ class Analyzer:
         """Interpolated target curve."""
         return self.interpolateCorrection(self._target)
 
-    def interpolateCorrection(self, corr: Correction) -> Optional[np.ndarray]:
+    def interpolateCorrection(
+            self, corr: Optional[Correction]) -> Optional[np.ndarray]:
         """
         Logarithmically interpolate the correction to a full-sized array.
         """
@@ -244,7 +245,7 @@ class Analyzer:
             dbRange: Maximum attenuation in dB (power).
             kaiserBeta: Shape parameter of the Kaiser tapering window.
             smoothing: Strength of frequency-dependent smoothing.
-            causality: 0 = linear-phase a-causal, 1 = minimal-phase causal.
+            causality: 0 = linear-phase a-causal, 1 = minimum-phase causal.
         """
         freq, H2 = self.H2(smoothing)
         # Apply target curve.
@@ -411,7 +412,7 @@ def transform_causality(x: np.ndarray, causality: float = 1) -> np.ndarray:
     the given impulse.
 
     Params:
-      causality: 0 = linear-phase, 1 = minimal-phase and
+      causality: 0 = linear-phase, 1 = minimum-phase and
         in-between values smoothly transition between these two.
 
     https://www.rle.mit.edu/dspg/documents/AVOHomoorphic75.pdf