This Statistics Fundamentals course provides a complete journey from basic descriptive statistics to core inferential statistics concepts. It begins with understanding the difference between population and sample, followed by essential descriptive measures such as mean, median, mode, range, interquartile range (IQR), variance, and standard deviation.
The course also explains why we use (n-1) instead of n when calculating variance and standard deviation for samples, making statistical reasoning clearer and more intuitive.
Learners will then explore the normal distribution and how it is used to interpret real-world data. Key advanced topics include the Central Limit Theorem, standard error of the mean (SEM), and how these concepts connect to sampling distributions.
The course continues with confidence intervals, the t-distribution, degrees of freedom, and hypothesis testing using one-sample t-tests and p-values. It also compares confidence intervals with t-tests to help learners understand how statistical decisions are made.
This course is ideal for students, researchers, and anyone interested in data analysis, research methods, or scientific statistics.
By the end of the course, learners will be able to analyze datasets, understand variability, interpret probability distributions, and apply inferential statistics confidently.
