ANOVA, or Analysis of Variance, is a statistical method used to compare the means of three or more groups. It extends the t-test, which is used for comparing two groups, to situations where more groups are involved. ANOVA helps to determine if at least one group mean is significantly different from the others, while controlling for multiple comparisons.

A t-test is a statistical method used to determine whether there is a significant difference between the means of two groups. It is one of the most commonly used hypothesis tests in statistics, especially when sample sizes are small, and the data is approximately normally distributed.

Probability distributions are mathematical functions that describe the likelihood of different outcomes in a random process. There are many types of probability distributions, but in this post, we will focus on five of the most common: the Normal, Binomial, Poisson, Exponential, and Uniform distributions.

Confidence intervals (CIs) are a fundamental concept in inferential statistics. They provide a range of values that are believed to contain the true population parameter (such as the mean) with a certain level of confidence. Rather than giving a single estimate, a confidence interval accounts for uncertainty in sampling and allows statisticians to express how confident they are in the estimate.

The standard error (SE) is a statistical measure that indicates the accuracy with which a sample mean represents the population mean. It is essentially the standard deviation of the sampling distribution of the sample mean. The smaller the standard error, the more precise the estimate of the population mean.

Standard deviation is a widely used measure of dispersion that tells us how spread out the values in a dataset are relative to the mean. It is a key statistic in both descriptive and inferential statistics, providing insight into the variability of data points around the average value.

Variance is a key concept in statistics that measures the spread or dispersion of a set of data points. It indicates how much the values in a dataset differ from the mean. A higher variance means that the data points are more spread out, while a lower variance indicates that they are closer to the mean.

The "mode" is a measure of central tendency that represents the value or values that occur most frequently in a dataset. Unlike the mean and median, the mode is specifically focused on identifying the most common value, making it useful for categorical or discrete data.

The "median" is another measure of central tendency in statistics. Unlike the mean, which sums up all the values and averages them, the median is the middle value in a sorted dataset. It provides a better sense of the typical value when dealing with skewed distributions or datasets with outliers.

In statistics, the "mean" is a measure of central tendency, which is used to represent the average value in a set of numbers. It is one of the most commonly used summary statistics because it provides a simple and clear way to understand the overall trend or level of the data.