# t-test vs Mann–Whitney U test strongly differ in p-value

I have a time series that is quite erratic, having lots of small increases and then large drops. I want to see if the mean is statistically significantly different to zero.

Using a t-test, I get a p-value of around 0.6, but using a wilcoxon ranksum test I get a p value of 0.0001.

How is this possible? I think the p-value of the t-test makes much more sense as I have only around 500 sample values, most of then +2 and some of them -200 so that the cumulative return of all of them is around 274. It should not be statistically significantly different to zero. Why does the ranksum test say otherwise? Why does it differ so much from the t-test?

df['returns'].fillna(0).std()
23.348037669415067
df['returns'].fillna(0).var()
545.1308630124249
df['returns'].fillna(0).mean()
0.5494399999999998
df['returns'].fillna(0).min()
-274.0
df['returns'].fillna(0).max()
93.04
df['returns'].fillna(0).median()
4.0
last value of cumsum:
274.72

stat, p_stat = ranksums(df['returns'].fillna(0).values, np.zeros(len(df['returns'].values)))
p_stat
2.9026104353437864e-32
stat, p_stat = ttest_ind(df['returns'].fillna(0).values, np.zeros(len(df['returns'].values)))
p_stat
0.598862760915118


The p_stat of the t-test is 0.6, but the p_stat of the ranksums is 2.9e-32. How can that be? For t-test I'm using scipy.stats.ttest_ind and for ranksums I use scipy.stats.ranksums.

• If the standard deviation of the data is 170, the standard error of the mean is $170/\sqrt{500} = 7.6$, which would lead to a t-statistic p-value of somewhere around machine precision; $2.9e-32$ wouldn't be far off. Did you leave out that $\sqrt{500}$ term by any chance? Commented Feb 11, 2018 at 20:24
• corrected the question and added more details Commented Feb 11, 2018 at 20:52
• What fraction of the values are $\leq 0$? Commented Feb 11, 2018 at 21:18
• 33% are <=0. Here the full time series: goo.gl/8Ucxhw Commented Feb 11, 2018 at 21:24
• You won't have independent identically distributed observations within either sample so neither test could be appropriate. However it's not clear why you'd expect the p-values to be similar; it's easy to get cases where means are essentially equal but P(X>Y) differs substantially from 1/2. Commented Feb 11, 2018 at 22:24

• Some more details: the p_stat of the t-test is 0.6, but the p_stat of the ranksums is 2.9e-32. How can that be? For t-test I'm using scipy.stats.ttest_ind and for ranksums I use scipy.stats.ranksums Commented Feb 11, 2018 at 19:51