May 30, 2019 · I finished analyzing my data and want to show that they are statistically significant using the t-test_ind. However, I haven't found anything functional to show this other than what was referenced in (How does one insert statistical annotations (stars or p-values) into matplotlib / seaborn plots?):

If you are using matplotlib and seeking boxplot annotation, use my code as a function: statistical annotation def AnnoMe(x1, x2, ARRAY, TXT): y, h, col = max(max(ARRAY[x1-1]),max(ARRAY[x2-1])) + 2, 2, 'k' plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=1.5, c=col) plt.text((x1+x2)*.5, y+h, TXT, ha='center', va='bottom', color=col)

Jan 5, 2013 · I am familiar with Python's scipy package, but I can't seem to find a way to test whether or not the two arrays are statistically significantly different at each individual array index. I'm thinking this is just a large 2D paired T-test, but am having difficulty.

Apr 2, 2021 · Most scientific publications use asterisks (*) to denote statistical significance in graphs. An example is shown below. This is rather simple in graphical programs such as Prism. Like many...

In statistics, statistical significance means that the result that was produced has a reason behind it, it was not produced randomly, or by chance. SciPy provides us with a module called scipy.stats, which has functions for performing statistical significance tests.

Oct 4, 2024 · Adding significance bars and asterisks to boxplots greatly enhances the interpretability of visualizations. By following the steps outlined in this article, you can effectively compare groups and visually represent the statistical significance of differences.

Let’s create a boxplot to which we can add significance bars. Specifically, let’s graph the petal length values and let’s use the boxplot() function from Matplotlib. This function takes a list as its input, and each element of this list is then used as the data for each group (box) in the boxplot.

This Python tool helps visualizing statistical significance on existing Matplotlib plots by adding significance bars and p-value labels between chosen pairs of columns. Features Converts p-values to asterisk notations for easy interpretation.

How do you obtain significance at each discrete data point rather than obtaining one t/p statistic for the comparison of the entire data set, such that you can plot of field of 5% significance. I plot and process data in Python, so bonus points for discussion how this …

Nov 5, 2022 · I'm tyring to compare a normal distribution to a histogram of actual data. I'm plotting the two with the following Python code: plt.hist(adjustedVar.iloc[:,8], bins=50, density=True) xmin, xmax = plt.xlim() x = np.linspace(xmin, xmax, 100) y = norm.pdf(x, meanAdj, stddevAdj) line = plt.plot(x, y) plt.show(line) Its looking ok: