Select basic ads. Create a personalised ads profile. Select personalised ads. Apply market research to generate audience insights. Measure content performance. Develop and improve products. List of Partners vendors. A t-test is a type of inferential statistic used to determine if there is a significant difference between the means of two groups, which may be related in certain features.
It is mostly used when the data sets, like the data set recorded as the outcome from flipping a coin times, would follow a normal distribution and may have unknown variances. A t-test is used as a hypothesis testing tool, which allows testing of an assumption applicable to a population. A t-test looks at the t-statistic, the t-distribution values, and the degrees of freedom to determine the statistical significance.
To conduct a test with three or more means, one must use an analysis of variance. Essentially, a t-test allows us to compare the average values of the two data sets and determine if they came from the same population. In the above examples, if we were to take a sample of students from class A and another sample of students from class B, we would not expect them to have exactly the same mean and standard deviation.
Similarly, samples taken from the placebo-fed control group and those taken from the drug prescribed group should have a slightly different mean and standard deviation. Mathematically, the t-test takes a sample from each of the two sets and establishes the problem statement by assuming a null hypothesis that the two means are equal. Based on the applicable formulas, certain values are calculated and compared against the standard values, and the assumed null hypothesis is accepted or rejected accordingly.
If the null hypothesis qualifies to be rejected, it indicates that data readings are strong and are probably not due to chance. The t-test is just one of many tests used for this purpose. Statisticians must additionally use tests other than the t-test to examine more variables and tests with larger sample sizes. For a large sample size, statisticians use a z-test. Other testing options include the chi-square test and the f-test. There are three types of t-tests, and they are categorized as dependent and independent t-tests.
Consider that a drug manufacturer wants to test a newly invented medicine. It follows the standard procedure of trying the drug on one group of patients and giving a placebo to another group, called the control group.
The placebo given to the control group is a substance of no intended therapeutic value and serves as a benchmark to measure how the other group, which is given the actual drug, responds.
After the drug trial, the members of the placebo-fed control group reported an increase in average life expectancy of three years, while the members of the group who are prescribed the new drug report an increase in average life expectancy of four years. Instant observation may indicate that the drug is indeed working as the results are better for the group using the drug. However, it is also possible that the observation may be due to a chance occurrence, especially a surprising piece of luck.
A t-test is useful to conclude if the results are actually correct and applicable to the entire population. While the average of class B is better than that of class A, it may not be correct to jump to the conclusion that the overall performance of students in class B is better than that of students in class A.
This is because there is natural variability in the test scores in both classes, so the difference could be due to chance alone. This includes rankings e. You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results. Discrete and continuous variables are two types of quantitative variables :.
Have a language expert improve your writing. Check your paper for plagiarism in 10 minutes. Do the check. Generate your APA citations for free! APA Citation Generator. Home Knowledge Base Statistics Statistical tests: which one should you use? Statistical tests: which one should you use?
They can be used to: determine whether a predictor variable has a statistically significant relationship with an outcome variable. Statistical tests flowchart Table of contents What does a statistical test do? What can proofreading do for your paper? What are the main assumptions of statistical tests? Statistical tests commonly assume that: the data are normally distributed the groups that are being compared have similar variance the data are independent If your data does not meet these assumptions you might still be able to use a nonparametric statistical test , which have fewer requirements but also make weaker inferences.
What is a test statistic? What is statistical significance? What is the difference between quantitative and categorical variables? What is the difference between discrete and continuous variables? Discrete and continuous variables are two types of quantitative variables : Discrete variables represent counts e.
Continuous variables represent measurable amounts e. Is this article helpful? For example, if a manager runs a pricing study to understand how best to price a new product, he will calculate the statistical significance — with the help of an analyst, most likely — so that he knows whether the findings should affect the final price. If the p-value comes in at 0. But what if the difference were only a few cents? But even if it had a significance level of 0.
In this case, your decision probably will be based on other factors, such as the cost of implementing the new campaign. Closely related to the idea of a significance level is the notion of a confidence interval. Say there are two candidates: A and B. The reason managers bother with statistical significance is they want to know what findings say about what they should do in the real world.
What would you do differently if the finding were different? Clean data and careful analysis are more important than statistical significance. Always keep in mind the practical application of the finding. On the other hand, the idea of variance decomposition can be interpreted as inference for the variances of batches of parameters sources of variation in multilevel regressions.
Chi-square test is a widely used hypothesis test method. Its applications in the statistical inference of classified data can be divided into two categories, one is the test of two rates or comparison of two composition ratios, and the other is the test of multiple rates or the comparison of multiple composition ratios and correlation analysis of classified data. Like all non-parametric statistics, the Chi-square is robust with respect to the distribution of the data.
Specifically, it does not require equality of variances among the study groups or homoscedasticity in the data.
It allows evaluation of both dichotomous independent variables and multiple groups of studies. Unlike many other non-parametric statistics and some parametric statistics, the calculations required in the Chi-square provide a great deal of information about how each of the groups performed in the study. Because of these sufficient information, researchers could fully aware of the results and obtain more detailed information from the statistic.
Advantages of the Chi-square are as follows: robustness with respect to distribution of the data, simple computation, detailed information from the test, availability in studies which is cannot be satisfied in parametric hypothesis , and flexibility in handling data from two or multiple group studies.
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