one sample t test python code

the one-sample t-test is a statistical method used to determine if there is a significant difference between the mean of a sample and a known or hypothesized population mean. in this tutorial, we'll walk through the process of conducting a one-sample t-test using python, with a code example.
make sure you have python installed on your system. you can install the necessary libraries using the following commands in your terminal or command prompt:
open your preferred python environment and import the necessary libraries:
create a sample dataset that you want to perform the one-sample t-test on. for example, let's say we have a sample of exam scores:
this generates a sample of 30 exam scores with a mean of 75 and a standard deviation of 10.
define the hypothesized mean that you want to test against. for instance, let's assume the population mean is 70:
the t_stat variable will store the t-statistic, and p_value will store the corresponding p-value.
print out the results and interpret whether the difference is statistically significant:
this code compares the p-value to a chosen significance level (alpha, typically set to 0.05). if the p-value is less than alpha, we reject the null hypothesis.
now you have a basic understanding of how to perform a one-sample t-test in python using the scipy library. remember to adapt the code to your specific dataset and research question, and always interpret the results in the context of your study.
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