diff --git a/infocalc.py b/infocalc.py
index 076ca3e..b55f8af 100755
--- a/infocalc.py
+++ b/infocalc.py
@@ -106,13 +106,14 @@ elif args.mode == "SIM":
 df = samplesize - 1 
     
 prob = 0
-if args.upperbound and args.lowerbound:
+
+if args.upperbound != None and args.lowerbound != None:
     prob_upper = t.cdf(df=df,x=args.upperbound, loc=mean, scale=stddev)
     prob_lower = t.cdf(df=df,x=args.lowerbound, loc=mean, scale=stddev)
     prob = prob_upper - prob_lower
-elif args.upperbound:
+elif args.upperbound != None:
     prob = t.cdf(df=df,x=args.upperbound, loc=mean, scale=stddev)
-elif args.lowerbound:
+elif args.lowerbound != None:
     prob = 1 - t.cdf(df=df,x=args.lowerbound, loc=mean, scale=stddev)
 else:
     prob = 1# no bounds set!
@@ -147,9 +148,9 @@ y = norm.pdf(x, loc=mean, scale=stddev)
 if args.normalizey:
     y = y * stddev#rescale back to unity area
 plt.axvline(x=mean, color="green", linestyle="dashed", label="mean")
-if args.lowerbound:
+if args.lowerbound != None:
     plt.axvline(args.lowerbound, color="red")
-if args.upperbound:
+if args.upperbound != None:
     plt.axvline(args.upperbound, color="red")
 
 plt.plot(x, y, 'b-', label='Normal distribution')