Choosing the right statistical test
1. Choosing the right statistical test
We'll now look into choosing the right statistical test for analyzing experimental data.2. Selecting the right test
Just as choosing the right book or the right measurement tool for is vital to research, choosing the right statistical test is foundational to any data analysis. Understanding our dataset's features and the hypotheses under examination is vital. It necessitates assessing the data types—categorical or continuous—their distributions, often assumed to be normal by many statistical tests, and the number of variables in the study. It's essential to align the chosen statistical method with the dataset's properties and the study's goals to ensure accurate and dependable outcomes. In this video, we'll explore how to apply t-tests, ANOVA, and Chi-Square tests, focusing on analyzing experimental data.3. The dataset: athletic performance
We'll work with a DataFrame called athletic_perf containing athletes' performance data, focusing on the effects of different training programs and diets on athletic performance. Key variables are the type of training program, assigned diet, initial fitness level, and the observed performance increase as a percentage.4. Independent samples t-test
An independent samples t-test is used to compare the means of two distinct groups to determine if there is a statistically significant difference between them. This test relies on the assumptions that the response data for both groups are normally distributed and have equal variances, ensuring the validity and reliability of the test results. We'll use an alpha of 0.5 and compare the mean athletic performance improvements between two groups undergoing High-Intensity Interval Training (HIIT) and Endurance training by assigning their performance increases to group1 and group2. Next we call ttest_ind on group1 and group2 and retrieve the test statistics and p-value. A large p-value here leads us to conclude that there is no significant difference in the mean performance increase between the HIIT and Endurance groups.5. One-way ANOVA
A one-way ANOVA test is employed to determine if there are statistically significant differences among the means of more than two groups. The one-way corresponds to ANOVA with a single independent variable, and it assumes that the variances among the groups are equal. For our example, we gather the athletic performance increase data for each training program type into a list of groups using a list comprehension. The f_oneway function from scipy.stats is then used to conduct the ANOVA test across these groups by unpacking the groups list using an asterisk. The relatively high P-value implies that, based on the provided data, we cannot confidently assert that different training programs lead to different mean increases in athletic performance.6. Chi-square test of association
The Chi-square test of association is a statistical method used to assess whether there is a significant association between two categorical variables. Unlike many other statistical tests, the chi-square test does not require assumptions about the distribution of the data. To prepare for the test, we start by creating a contingency table using crosstab from pandas, which cross-tabulates athletes by their Training_Program and Diet_Type.7. Chi-square test of association
The chi2_contingency function from scipy.stats is then employed to conduct the chi-square test on the contingency table. The large P-value suggests that any observed association between training programs and diet types is not statistically significant.8. Let's practice!
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