Confidence level and significance level are two important concepts in statistical hypothesis testing. The confidence level measures how confident we are that our conclusions are correct. In contrast, the significance level (also called alpha value) i...

In hypothesis testing, two types of errors can occur: type I and type II. These errors refer to the incorrect rejection or acceptance of the null hypothesis respectively.Type I Error (Alpha)A type I error occurs when the null hypothesis is true but i...

In statistics, the null and alternative hypotheses are two mutually exclusive and exhaustive hypotheses used in hypothesis testing to evaluate the evidence in a sample. The null hypothesis represents the default assumption that no significant differe...

Hypothesis testing is a statistical procedure that allows us to test assumptions or beliefs about a population based on sample data. There are two main approaches to hypothesis testing:Traditional approach andThe p-value approach.In the traditional a...

Hypothesis testing is a statistical procedure that allows us to test assumptions or beliefs about a population based on sample data. It is a statistical procedure that is used to determine whether a hypothesis about a population parameter is supporte...

A normal probability plot is a graphical representation of a data set used to assess whether the data follows a normal (bell-shaped) distribution. It is similar to a quantile-quantile plot (Q-Q Plot), which plots the quantiles of the data set against...

Have you ever encountered a stem and leaf plot and wondered what it is and how to interpret it? If so, you're not alone. While stem and leaf plots may seem confusing initially, they are a simple and effective way to visualize data and understand its...

Data visualization is a crucial aspect of any data analysis or presentation. It allows us to quickly and easily understand patterns and trends in the data and make informed decisions based on this information. One helpful tool for visualizing data is...

 Standard deviation is a measure of how spread out a dataset is. It is calculated by finding the difference between each data point in the dataset and the mean (average) of the dataset, squaring those differences, finding the average of the squared d...

The interquartile range (IQR) is a measure of the dispersion of a dataset. It is calculated as the difference between the 75th percentile (Q3) and the 25th percentile (Q1) of the data and is a way to identify the spread of the middle 50% of the data....