Welcome to On Statistics

In statistical analysis, we often encounter the terms z-score and p-value. Both concepts are essential in hypothesis testing and data analysis. However, their relationship might not be immediately clear. In this article, we’ll discuss how to find a p-value from a z-score using Python. What is a Z-Score? A z-score is a standardized value that…

The Normal Cumulative Distribution Function (CDF) is an essential concept in statistics and probability theory. It describes the probability that a normally distributed random variable X with mean μ and standard deviation σ takes on a value less than or equal to a given value x. In other words, it returns the probability that X…

In statistical analysis, the t-test is a common method used to determine if there is a significant difference between the means of two independent groups. The t-test results in a t-score, which measures the difference between the two means relative to the variability of the data. However, to determine the significance of the observed difference,…

Percentiles are statistical measures that help to understand the distribution of data. They divide a dataset into 100 equal parts, each part representing one percentile. The value of a given percentile is the value below which a certain percentage of observations falls. For instance, the 50th percentile is the median, the value below which 50%…

Curve fitting is a statistical method used to establish a mathematical relationship between a set of data points and a continuous function. The goal is to find the best-fitting curve that approximates the data, allowing for predictions and analysis of trends. In this article, we will explore the concept of curve fitting, its underlying statistical…

The binomial distribution is a fundamental concept in probability theory and statistics. It describes the probability of obtaining a certain number of successes (x) in a fixed number of independent experiments (n), where each experiment has only two possible outcomes: success (S) or failure (F). These outcomes are often represented as 1 (success) and 0…

Cluster sampling is a type of probability sampling where the population is first divided into clusters or groups, and then a random sample is selected from each of these clusters. This method is often used when it is not feasible or cost-effective to survey the entire population. In this tutorial, we will learn how to…

Systematic sampling is a probability-based method for selecting a subset of observations from a larger dataset. In this technique, we select every nth observation from the dataset, where n is a predefined number. This method is particularly useful when we want to ensure that the sample is representative of the population and that the data…

Hypothesis testing is a statistical method used to evaluate whether a hypothesis about a population parameter is true or false based on a sample. It is an essential tool in statistics and data analysis. In this article, we will discuss the concept of hypothesis testing, its importance, and how to perform it using Python. The…

Sampling with replacement, also known as resampling with replacement, is a statistical technique where you draw observations from a finite population and then return them to the pool before the next draw. This method is different from simple random sampling without replacement, where you draw an observation and do not replace it before the next…