The Power of Evidence-Based Decision Making — Are you curious about how statisticians determine whether a claim is true or false? Do you want to know how statisticians make decisions based on data instead of just gut feeling? Look no further, because today we’re diving into the world of hypothesis testing! What is Hypothesis Testing in Statistics? In statistics, hypothesis testing is a…

Alternative ways to write for-loop in Python — Have you ever needed to create a list in Python, but didn’t want to use a loop to do it? List comprehension is a concise way to create a list using a single line of code. But how does it work, and when should you use it? …

Have you ever encountered an outlier in your data — a data point that is significantly different from the majority of the other points? …

Precision and accuracy are two important concepts in the field of measurement and are often used interchangeably, but they do have different meanings. …

How to properly collect samples for a statistical analysis — Sample methods in statistics are techniques that are used to collect and analyze data from a subset of a population in order to make inferences about the population as a whole. …

Degrees of freedom is a term used in statistics to describe the number of independent observations in a sample or a statistical test. It is an important concept because it determines the precision and reliability of the sample estimates and statistical tests. What are degrees of freedom? The degrees of freedom are defined as the…

Random error and systematic error are two types of measurement errors that can occur when collecting and analyzing data. Understanding the difference between these two types of error is crucial in order to properly interpret and analyze the data.

Nonparametric tests and parametric tests are two types of statistical tests that are used to analyze data and make inferences about a population based on a sample. Nonparametric tests are used when the data do not follow a normal distribution or when the assumptions of parametric tests are not met…

Tips for Hypothesis Testing — Statistical significance and practical significance are two important concepts in statistical analysis that are often misunderstood or conflated. Understanding the distinction between these two concepts is crucial for the proper interpretation and application of research findings. What is the statistical significance? Statistical significance refers to the likelihood that an observed result or relationship between variables…