...A "Z Test" is a statistical test used to compare two population means. It is usually used when the two populations being compared have different standard deviations and/or sample sizes. The "Z Test" can also be used to compare a single sample mean to a population mean. The basic procedure for the "Z Test" involves calculating a test statistic, called the “Z Score”, which measures the difference between the two population means in standard deviation units. The Z Score is then used to calculate the probability that the two population means come from the same population. The "Z Test" is a powerful way of analyzing data, and it is widely used in the fields of medicine, economics, and marketing. With the right knowledge and skills, the "Z Test" can be a useful tool for evaluating hypotheses and testing theories. Read more here.
The "Student T Test" is a widely used statistical test that's used to compare two samples to see if there's a statistically significant difference between them. It's based on a comparison of the means of the two samples, and it relies on what's known as the t-distribution to determine the probability of a given set of results occurring by chance. The "Student T Test" can be used to compare two means, and to determine the significance of one mean compared to another. It's also used to compare one sample to a known value or to test the significance of a coefficient in a regression analysis. It's an important tool in the field of statistics, and it's widely used in fields such as economics, sociology, psychology, and biology.
ANOVA, or Analysis of Variance, is a statistical method used to test differences between the means of two or more groups of data. It's used to determine if there's a significant difference between the means of different groups. ANOVA can be used for various types of research, such as studies that compare different treatments or conditions, or experiments that involve multiple independent variables. To conduct an ANOVA, we first calculate the variance within each group as well as the overall variance from all groups. Then, we compare the between-group variance and the within-group variance to see if there's a significant difference between the means of the groups. This can help us determine if the differences between the groups are due to random chance or if they are the result of some underlying factor. ANOVA is a powerful tool for analyzing data and can be used for a variety of applications in research.
Correlation is a statistical measure of the relationship between two or more variables. It is used to determine how closely the values of two or more variables are associated with each other. Correlation statistics can help us understand how changes in one variable may be related to changes in another. For example, a correlation statistic could be used to determine whether a change in temperature is associated with a change in precipitation. Correlation can also help us identify cause and effect relationships. If two variables have a strong correlation, then it is likely that one is causing the other to change. Correlation can also be used to identify patterns and trends in data. By analyzing the correlation between two variables, we can gain insight into how the variables interact with each other and can identify potential relationships. Correlation is an important statistic in the field of data analysis, and it is one of the most used statistical measures.
Regression Analysis is a statistical technique used to identify relationships between variables. It is a powerful tool for predicting future outcomes and identifying the relationships between different variables. It is used by businesses and researchers to better understand their data and make informed decisions. Regression Analysis can help to identify potential problems, uncover trends, and develop models to forecast future events. It can also be used to identify influential variables and to determine the most important factors in a given situation. Regression Analysis is a powerful tool that can be used to gain insights into complex systems, and to make better decisions. With the right data and the right method, Regression Analysis can help to unlock the hidden knowledge in your data, providing invaluable insights.
Factor Analysis Statistics is a powerful tool used to measure and analyze the relationships between variables in a dataset. It is used to identify patterns in data, which can be useful for things like identifying customer segments or predicting future trends. Factor Analysis Statistics also allows researchers to identify underlying factors or latent variables in a dataset, which can be used to gain a better understanding of the data. Factor Analysis Statistics is a relatively simple technique with a wide range of applications. It can be used to identify relationships between variables, uncover hidden patterns in data, and make predictions about future trends. It is an invaluable tool for researchers, and it can be used in any field that requires analysis of complex data.
Statistics can be divided into two broad categories: parametric and nonparametric. Parametric statistics involve making assumptions about the data, while nonparametric statistics do not. The most common type of parametric statistic is the mean, which assumes that the data follow a normal distribution. Nonparametric statistics, on the other hand, make no assumptions about the data and are often used when the data is not normally distributed. Parametric statistics can be used to calculate a variety of measures, including correlation, regression, and ANOVA. Nonparametric statistics, on the other hand, can be used for tests such as the Mann-Whitney U test, the Wilcoxon signed-rank test, and the Kruskal-Wallis test. Both types of statistics have their advantages and disadvantages, and it is important to choose the appropriate type of statistic for the data being analyzed.
Sources and Further Reading.
Bangdiwala, Shrikant I. "The teaching of the concepts of statistical tests of hypotheses to non-statisticians." Journal of Applied Statistics 16.3 (1989): 355-361.
Camargo, Anyela, et al. "Permutation–based statistical tests for multiple hypotheses." Source code for biology and medicine 3.1 (2008): 1-8.
Freund, Rudolf J., William J. Wilson, and Ping Sa. Regression analysis. Elsevier, 2006.
Greenland, Sander, et al. "Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations." European journal of epidemiology 31.4 (2016): 337-350.
Hedges, Larry V., and Therese D. Pigott. "The power of statistical tests in meta-analysis." Psychological methods 6.3 (2001): 203.
Mathew, Thomas, Bimal K. Sinha, and André I. Khuri. Statistical tests for mixed linear models. John Wiley & Sons, 2011.
McCrum-Gardner, Evie. "Which is the correct statistical test to use?." British Journal of Oral and Maxillofacial Surgery 46.1 (2008): 38-41.
Miller Jr, Rupert G. Beyond ANOVA: basics of applied statistics. CRC press, 1997.
Neideen, Todd, and Karen Brasel. "Understanding statistical tests." Journal of surgical education 64.2 (2007): 93-96.