diff --git a/README.md b/README.md
index cdc9c2cc75a1f464f169f4f0f582e9fa2a9923f2..15348509257dd4ec8f82e0b2d6a2cccd325cb510 100644
--- a/README.md
+++ b/README.md
@@ -38,7 +38,7 @@ The `--user` argument for `pip` installs the python package in your home
 directory, which hides potentially older packages installed with `apt-get`.
 
 ## Windows
-Since I'm not using python on Windows myself, I don't have first-hand experiance
+Since I'm not using python on Windows myself, I don't have first-hand experience
 with it. However I think [Anaconda](https://www.continuum.io/downloads) is a
 good solution for windows users, which provides all required packages. 
 
@@ -166,7 +166,7 @@ The result is [-1.   0.5  2.   5.   6.5]
 ```
 
 Numpy offers many other functionalities which are beyond the scope of this basic
-introduction. It is definetely worth glancing at the
+introduction. It is definitely worth glancing at the
 [documentation](https://docs.scipy.org/doc/numpy/index.html).
 
 # Plotting Functions
@@ -399,7 +399,7 @@ numpy array. The function returns a two dimensional array. The outer array has
 one entry for each line in the text file. The inner array has two entries in our
 case, one for the
 channel and the other one for the event count. We can use `transpose()` to flip
-the matrix, such that the outer array has tswo entries, one with an array of
+the matrix, such that the outer array has two entries, one with an array of
 channel values and the other one with event counts. Since our measured event
 counts stem from radioactive decay, we know that the event counts follow a
 Poisson distribution. Therefore, the uncertainties of the event counts are simply
@@ -614,7 +614,7 @@ laboratories. Here is a list with online resources, which might be useful.
 
  - [Python](https://docs.python.org/3/) documentation
  - [Numpy and Scipy](https://docs.scipy.org/doc/) documentation
- - [Matplotlib](http://matplotlib.org/contents.html) documentaiton
+ - [Matplotlib](http://matplotlib.org/contents.html) documentation
  - [Orthogonal distance regression](https://docs.scipy.org/doc/scipy/reference/odr.html) - least square fit, which considers
    uncertainties in $`x`$ and $`y`$ directions.
  - Histograms in