diff --git a/convoys/__init__.py b/convoys/__init__.py
index 3d183e4..1e66876 100644
--- a/convoys/__init__.py
+++ b/convoys/__init__.py
@@ -140,6 +140,7 @@ def plot_cohorts(data, t_max=None, title=None, group_min_size=0, max_groups=100,
     # PLOT
     colors = seaborn.color_palette('hls', len(groups))
     y_max = 0
+    result = []
     for group, color in zip(sorted(groups), colors):
         X, B, T = get_arrays(js[group], t_converter)
         t = numpy.linspace(0, t_max, 1000)
@@ -157,6 +158,7 @@ def plot_cohorts(data, t_max=None, title=None, group_min_size=0, max_groups=100,
             label += ' projected: %.2f%% (%.2f%% - %.2f%%)' % (100.*p_y_final, 100.*p_y_lo_final, 100.*p_y_hi_final)
             pyplot.plot(t, 100. * p_y, color=color, linestyle=':', alpha=0.7)
             pyplot.fill_between(t, 100. * p_y_lo, 100. * p_y_hi, color=color, alpha=0.2)
+            result.append((group, p_y_final, p_y_lo_final, p_y_hi_final))
 
         m_t, m_y = m.predict([1], t)
         pyplot.plot(m_t, 100. * m_y, color=color, label=label)
@@ -170,6 +172,7 @@ def plot_cohorts(data, t_max=None, title=None, group_min_size=0, max_groups=100,
     pyplot.ylabel('Conversion rate %')
     pyplot.legend()
     pyplot.gca().grid(True)
+    return result
 
 
 def plot_timeseries(data, window, model='kaplan-meier', group_min_size=0, max_groups=100, window_min_size=1, stride=None, title=None, time=False):
diff --git a/test_convoys.py b/test_convoys.py
index 940afc8..50ba4d8 100644
--- a/test_convoys.py
+++ b/test_convoys.py
@@ -86,27 +86,25 @@ def test_gamma_regression_model(c=0.3, lambd=0.1, k=3.0, n=100000):
     assert 0.90*lambd < numpy.exp(model.params['alpha']) < 1.10*lambd
 
 
-def _get_data(c=0.3, k=10, lambd=0.1, n=1000):
+def test_plot_cohorts(cs=[0.3, 0.5, 0.7], k=2.0, lambd=0.1, n=100000):
+    C = numpy.array([bool(random.random() < cs[r % len(cs)]) for r in range(n)])
+    N = scipy.stats.uniform.rvs(scale=5./lambd, size=(n,))
+    E = numpy.array([sample_weibull(k, lambd) for r in range(n)])
+    B, T = generate_censored_data(N, E, C)
     data = []
-    now = datetime.datetime(2000, 7, 1)
-    for x in range(n):
-        date_a = datetime.datetime(2000, 1, 1) + datetime.timedelta(days=random.random()*100)
-        if random.random() < c:
-            delay = scipy.stats.gamma.rvs(a=k, scale=1.0/lambd)
-            date_b = date_a + datetime.timedelta(days=delay)
-            if date_b < now:
-                data.append(('foo', date_a, date_b, now))
-            else:
-                data.append(('foo', date_a, None, now))
-        else:
-            data.append(('foo', date_a, None, now))
-    return data
-
-
-def test_plot_cohorts():
-    convoys.plot_cohorts(_get_data(), projection='gamma')
-
-
-@pytest.mark.skip
-def test_plot_conversion():
-    convoys.plot_timeseries(_get_data(), window=datetime.timedelta(days=7), model='gamma')
+    x2t = lambda x: datetime.datetime(2000, 1, 1) + datetime.timedelta(days=x)
+    for i, (b, t, n) in enumerate(zip(B, T, N)):
+        data.append(('Group %d' % (i % len(cs)),  # group name
+                     x2t(0),  # created at
+                     x2t(t) if b else None,  # converted at
+                     x2t(n)))  # now
+
+    result = convoys.plot_cohorts(data, projection='weibull')
+    group, y, y_lo, y_hi = result[0]
+    c = cs[0]
+    k = len(data)/len(cs)
+    c_lo = scipy.stats.beta.ppf(0.025, k*c, k*(1-c))
+    c_hi = scipy.stats.beta.ppf(0.975, k*c, k*(1-c))
+    assert group == 'Group 0'
+    assert 0.95*c < y < 1.05 * c
+    assert 0.70*(c_hi-c_lo) < (y_hi-y_lo) < 1.30*(c_hi-c_lo)