Course curriculum

    1. 1910 Sampling

    2. 1915 Central Limit Theorem (CLT)

    3. 1920 Standard Error

    1. 1910-1 Central Limit Theorem Pop Data Skewed to the Right

    2. 1921 Central Limit Theorem – All Possible Samples Example

    3. 1925-6 Standard Error, Estimated Standard Deviation of Xbar Correction Factor

    4. 1930 Standard Deviation of Means Estimate Example

    5. 1936-7 Binomial Distribution Standard Deviation of Sample Means or Standard Error (1)

    6. 1941 Standard Error, Margin of Error, Hypothesis Test, _ Confidence Interval

    1. 1910 Central Limit Theorem Pop Data Skewed to the Right

    2. 1920 Central Limit Theorem – All Possible Samples Example

    3. 1925 Standard Error, Estimated Standard Deviation of Xbar Correction Factor

    4. 1930 Standard Deviation of Means Estimate Example

    5. 1936 Binomial Distribution Standard Deviation of Sample Means or Standard Error

    6. 1938 Binomial Distribution Standard Deviation of Sample Means or Standard Error Part 2

    7. 1940 Standard Error, Margin of Error, Hypothesis Test, & Confidence Interval

    8. 1942 Standard Error, Margin of Error, Hypothesis Test, & Confidence Interval Part 2

About this course

  • $49.99
  • 17 lessons
  • 9.5 hours of video content

Description

This course offers a foundational introduction to the principles of statistics, focusing on sampling techniques, the Central Limit Theorem (CLT), and the concept of standard error. Students will explore the process of selecting representative samples from larger populations, a crucial step in making valid statistical inferences. Various sampling methods, such as simple random sampling, stratified sampling, cluster sampling, and systematic sampling, will be covered in detail, enabling students to understand how to collect data that accurately represents a broader group. The importance of sampling in real-world applications will be emphasized, including considerations of bias and sampling error that can impact the validity of conclusions drawn from sample data.

A central focus of the course is the Central Limit Theorem, a key statistical concept that underpins much of inferential statistics. Through examples and hands-on exercises, students will learn how the CLT allows statisticians to approximate the distribution of sample means as normal, even when the population distribution is not normal. This property is foundational to many statistical methods, such as hypothesis testing and confidence interval estimation. Understanding the CLT enables students to appreciate the role of sample size, as larger samples yield distributions of sample means that are more consistently normal and provide a closer approximation of population parameters.

The course also introduces the concept of standard error, which measures the variability of a sample statistic, such as the sample mean, around the true population parameter. Students will examine how standard error reflects the precision of sample estimates and how it can be minimized through increased sample sizes. Applications of standard error in constructing confidence intervals and performing hypothesis tests will be covered, allowing students to quantify uncertainty and make informed inferences based on sample data.

Throughout the course, students will work on practical examples that demonstrate the applications of statistical concepts across various fields, such as social science research, economics, and quality control. These examples will illustrate how sampling, the CLT, and standard error are applied in real-world scenarios to draw conclusions about larger populations from sample data. By the end of the course, students will be equipped with essential statistical tools and techniques, laying the groundwork for more advanced studies in statistics and data analysis. This course is designed for students beginning their exploration of statistical methods, providing a robust introduction to the basics of data collection, analysis, and inference.