In this tutorial, you will learn about the chi-square test and its application. By examining the relationship between the elements, the chi-square test aids in the solution of feature selection problems. Feature selection is a critical topic in machine learning, as you will have multiple features in line and must choose the best ones to build the model. The world is constantly curious about the Chi-Square test's application in machine learning and how it makes a difference. The Most Comprehensive Guide for Beginners on What Is Correlation Lesson - 24 Your Best Guide to Understand Correlation vs. The Complete Guide to Understand Pearson's Correlation Lesson - 20Ī Complete Guide on the Types of Statistical Studies Lesson - 21Įverything You Need to Know About Poisson Distribution Lesson - 22 The Complete Guide to Skewness and Kurtosis Lesson - 15Ī Holistic Look at Bernoulli Distribution Lesson - 16Īll You Need to Know About Bias in Statistics Lesson - 17Ī Complete Guide to Get a Grasp of Time Series Analysis Lesson - 18 The Definitive Guide to Understand Spearman’s Rank Correlation Lesson - 12Ī Comprehensive Guide to Understand Mean Squared Error Lesson - 13Īll You Need to Know About the Empirical Rule in Statistics Lesson - 14 Understanding the Fundamentals of Arithmetic and Geometric Progression Lesson - 11 The Best Guide to Understand Bayes Theorem Lesson - 6Įverything You Need to Know About the Normal Distribution Lesson - 7Īn In-Depth Explanation of Cumulative Distribution Function Lesson - 8Ī Complete Guide to Chi-Square Test Lesson - 9Ī Complete Guide on Hypothesis Testing in Statistics Lesson - 10 The Ultimate Guide to Understand Conditional Probability Lesson - 4Ī Comprehensive Look at Percentile in Statistics Lesson - 5 The Best Guide to Understand Central Limit Theorem Lesson - 2Īn In-Depth Guide to Measures of Central Tendency : Mean, Median and Mode Lesson - 3 Now that you know the differences between the two, a few types of each, and some examples of how they're used, you can make an informed decision on which is best for your business.Everything You Need to Know About the Probability Density Function in Statistics Lesson - 1 These samples are chosen by researchers just because they're simple to recruit and the researchers don't consider choosing a sample that represents the whole population. Taking convenience sampling as an example, this is a non-random sampling method where samples are chosen from the population only because they're available conveniently to the researcher. There are several types of non-random sampling such as: This method is used in studies by researchers where it's impossible to draw random sampling because of cost and time considerations. Non-random sampling is used most often for exploratory studies such as pilot surveys (you deploy a survey tool to a smaller sample when you compare it to a predetermined sample size). This means there are limits to the amount you can determine from the sample about the population. With this form of sampling survey tool, you exclude a certain amount of the population in the sample and you can't calculate that exact number. Through this method, you pick the sample size you desire and select observations from the population in a manner that each observation has the same likelihood of selection until you achieve the desired sample size. Taking simple random sampling as an example, this type of sampling survey software is the most straightforward method of obtaining a random sample. It's usually assumed the statistical testing contains information that has been collected through random sampling.Īn example of when you'd do this type of sampling is exit polls from voters looking to predict an election's results.ĭifferent types of random sampling online survey software are: The selection needs to occur "randomly", which means they don't differ in any substantial way from observations that aren't sampled. With random sampling, or probability sampling, you begin with a complete sample frame of all qualified people that have the same likelihood of being part of the chosen sample. Non-random sampling (non-probability sampling), which involves non-random selection based on criteria like the convenience that allows you to collect initial data easily.Random sampling (probability sampling), which involves random selection that allows you to make statistical inferences about the entire group.Basically, you have two types of sampling techniques: This sample is the group of people who will be participating in the research.įor you to draw legitimate conclusions from the results you obtain, you need to make a careful decision on how you'll select a sample that represents the group as a whole. When you're conducting research about a group of individuals it's hardly possible for you to gather data on each and every person in the group. Posted on by Elizabeth in category: survey software articles
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