{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# $t$-Tests\n",
"\n",
"***\n",
"\n",
"$t$-tests are among the most common statistical tests performed in world.\n",
"\n",
"This notebook focuses on the practicalities of performing $t$-tests in Python.\n",
"\n",
"For information about the $t$-test itself, I recommend reading [Laerd Statistics's Independent t-test using SPSS Statistics](https://statistics.laerd.com/spss-tutorials/independent-t-test-using-spss-statistics.php)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Packages\n",
"***\n",
"\n",
"One of Python's strengths is the quality of numerical packages available.\n",
"\n",
"
"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Efficient numerical arrays.\n",
"import numpy as np\n",
"\n",
"# Data frames.\n",
"import pandas as pd\n",
"\n",
"# Alternative statistics package.\n",
"import statsmodels.stats.weightstats as stat\n",
"\n",
"# Mains statistics package.\n",
"import scipy.stats as ss\n",
"\n",
"# Plotting.\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Fancier plotting.\n",
"import seaborn as sns\n",
"\n",
"# Better sized plots.\n",
"plt.rcParams['figure.figsize'] = (12, 8)\n",
"\n",
"# Nicer colours and styles for plots.\n",
"plt.style.use(\"ggplot\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"\n",
"#### Fake data values\n",
"\n",
"***\n",
"\n",
"We can create fake data sets with specific properties to investigate numerical methods.\n",
"\n",
"
"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
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