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Here are some examples of real-world statistical analyses that don’t use p-values and significance testing Your results from a hypothesis test are statistically significant. Bravo! Are these results important? Not so. Significance not necessarily mean the results are practically significant in real world Statistical Analysis . In this blog post, I will list out few examples of real world statistical analyses that do not use p-value and significance testing. Before that, let us understand what exactly the p-value mean?                             Statswork is one among the country’s leader in providing  Data Analysis Services  and Statistical Consulting Services.  Contact Statswork for availing our services. P-value The same scenario as above have been faced by many data scientist when we talk about the p-value, isn’t it? Well, sometimes the simplest definition of p-value tend to ...

ANOVA is a statistical tool used for comparing statistical groups using the dependant and the independent variables. Analysis of variance (ANOVA) is a technique that uses a sample of observations to compare the number of means. ANOVA calculates statistical differences between two or more means for either groups or variances. The measured variables are called dependent variable e.g. Test score, while the variables which are controlled are termed as independent variable e.g. Test paper correction method. Statswork is one among the country’s leader in providing ANOVA and statistical consultancy services.  Contact Statswork for availing our services. Analysis of variance Analysis of variance (ANOVA) is a statistical technique which is used to compare datasets. It is commonly referred to as Fisher’s ANOVA or Fisher’s analysis of variance . It is similar to that of t-test and z-test, which are used to compare mean along with relative variance. However, in ANOVA, it is best ...

SEM using AMOS Structural Equation Modelling (SEM) is a widely used technique in statistics to primarily study relationships based on structures. It encompasses various models involving mathematics, statistical procedures etc. This technique is known to be extremely effective when it comes to measuring latent constructs. Many of us might be familiar with concepts like Multiple Regression Analysis and Factor Analysis , this in simple term, is a combination of these techniques. It is, in fact, a mere extension of the General Linear Model. You can test a bunch of regression techniques at the same time. Structural Equation Modelling includes a model that makes room for a lot of other statistical techniques such as path analysis, confirmatory factor analysis and latent growth modelling etc. This is impressive as SEM as a type of model covers many models that are both traditional and complex. It is also effective in the assessment of variance and Multiple Regression along with e...