diff --git a/README.md b/README.md
index 7f94108e463ffd135eee53a27e4b296cb1d98a85..52e8fa69ddfb8fe5efc6a90ab6e90bdeaa60db94 100644
--- a/README.md
+++ b/README.md
@@ -566,20 +566,20 @@ the uncertainties are the square roots of the expected number of events. If this
 case, we have to compute the $`\chi^2`$ manually. The following example shows
 both, the usage of `chisquare` and the manual computation. The print statements
 for both methods produce the same output. Please note that
-our model has five degrees of freedom since we have five free parameters. The
+our model has five free parameters. The
 total number of degrees of freedom is the number of data points reduced by the
-number of degrees of freedom of our model.
+of parameters of our model.
 
 <!-- append decay.py -->
 ```python
-# Degrees of freedom of our model.
-dof = 5
+# Delta degrees of freedom
+ddof = 5
 
 print("chi^2 from scipy:")
 
 # Call chisquare and print.
-chi2, p = scipy.stats.chisquare(count, fit_count, ddof=dof)
-print("  chi2 / dof = %g / %d" % (chi2, len(count) - dof))
+chi2, p = scipy.stats.chisquare(count, fit_count, ddof=ddof - 1)
+print("  chi2 / dof = %g / %d" % (chi2, len(count) - ddof))
 print("  p-value = %g" % p)
 
 print()  # print blank line
@@ -591,17 +591,17 @@ uncertainty = np.sqrt(fit_count)
 chi2 = ((count - fit_count)**2 / uncertainty**2).sum()
 
 # Calculate p-value and print
-p = scipy.stats.distributions.chi2.sf(chi2, len(count) - 1 - dof)
-print("  chi2 / dof = %g / %d" % (chi2, len(count) - dof))
+p = scipy.stats.distributions.chi2.sf(chi2, len(count) - ddof)
+print("  chi2 / dof = %g / %d" % (chi2, len(count) - ddof))
 print("  p-value = %g" % p)
-
 ```
 
-The additional `-1` stems from the fact that we have a Poisson distribution
-for which the uncertainties are derived from the number of events. The
-test is not sensitive to the normalization, and therefore, we loose one degree
-of freedom, cf. [Data Analysis in High Energy
-Physics](https://www.wiley.com/en-us/Data+Analysis+in+High+Energy+Physics%3A+A+Practical+Guide+to+Statistical+Methods-p-9783527410583) by Behnke et at.
+The additional `- 1` in the call of `chisquare` is necessary because the method
+implicitly assumes that the total number of events is fixed. The method
+therefore reduces the number of degrees of freedom by one. However, in our case,
+the number of decays is not fixed and we have an additional degree
+of freedom compared to what `chisquare` assumes. The `- 1` in the `ddof`
+corrects for this.
 
 If you run the `decay.py` you should see the following fit results.
 <!-- console_output -->
@@ -616,11 +616,11 @@ Optimal parameters:
 
 chi^2 from scipy:
   chi2 / dof = 120.577 / 123
-  p-value = 0.519418
+  p-value = 0.544943
 
 Manual chi^2 test:
   chi2 / dof = 120.577 / 123
-  p-value = 0.519418
+  p-value = 0.544943
 ```
 
 Congratulations! You have mastered the first steps to analyze experimental data