How do you select the right statistical test for your experimental data?

Selecting the right statistical test depends on several factors, including the type of data you have (e.g., continuous, categorical), the number of groups or variables you are comparing, and the specific hypothesis you want to test. Common tests include t-tests for comparing means, ANOVA for comparing multiple groups, chi-square tests for categorical data, and correlation/regression for assessing relationships. Consulting with a statistician or using online guides can help ensure you choose the most appropriate test for your data and research question.

Determine whether your data is categorical or continuous, and the level of measurement (nominal, ordinal, interval, or ratio).Clearly articulate what you want to investigate or compare in your data.

Before selecting the right statistical test for your experimental data, it is very crucial to understand the type of your variables if categorical or numerical, also make sure to specify the dependent and independent variables

The type of data is the starting point , then the probability distributions of the variables and scope through some descriptive statistics help you to understand the structure. The results of these initial operations on your data will guide you on the kind of subsequent statistical tests to perform

With large sample sizes, even small differences between groups or correlations can reach statistical significance.Some tests, like t-tests and ANOVA, assume normality of data, and this assumption becomes more critical with smaller sample sizes.For categorical data, the chi-square test can be sensitive to small expected cell frequencies, so larger sample sizes are preferred

The sample size is one of the most important thing when deciding what statistical test to choose for your experimental data, parametric tests like ANOVA, T-tests require working in large sample size and assuming a normal distribution, while non-parametric tests do not assume a specific distribution

Research question: Clearly define what you want to investigate or compare in your data. For example, are you interested in comparing means between groups, examining associations between variables, or testing for differences in proportions? Hypothesis: Formulate a null hypothesis (H0) and an alternative hypothesis (H1) based on your research question. The null hypothesis typically posits no effect or no difference, while the alternative hypothesis proposes the presence of an effect or difference. Outcome measure: Determine the outcome measure(s) that will be used to assess the hypothesis. This could be means, proportions, correlations, differences in ranks, etc.

depending on your research question or hypothesis, you might testing the association between categorical variables so the Chi-Square might be the right statistical test, or you might checking if there any statistical significance difference in the means across group levels so you need to use ANOVA or t-tests

The type of hypothesis you formulate depend on the two main types of hypotheses: Null hypothesis (H0): This hypothesis represents the default assumption or the status quo. It states that there is no effect, no difference, or no relationship between variables in the population being studied. For example, "There is no difference in mean scores between Group A and Group B." Alternative hypothesis (H1): Also known as the research hypothesis, the alternative hypothesis proposes a specific effect, difference, or relationship between variables. It contradicts the null hypothesis and is what you are trying to find evidence for in your study. For example, "There is a difference in mean scores between Group A and Group B."

Assumption check involves verifying if data meet underlying assumptions of statistical tests: normality, homogeneity of variance, independence, linearity (for regression), absence of multicollinearity (for regression), and random sampling.

It is very important to check your assumptions before conducting any statistical test like testing the normality of a dependent continuous variable and homogeneity of variance when conducting Anova test

Navigating the maze of statistical tests can feel like choosing the right tool from a vast toolbox. Each test serves a unique purpose, much like different tools in a craftsman's workshop. It's crucial to consider the nature of your data (continuous, categorical). Then, ponder the size of your sample; high statistical power will require larger samples. But wait, there's more! What's the goal of your hypothesis? Inequality, superiority, non-inferiority, or equivalence? Your choice of test should align with your research question's heartbeat. In essence, selecting the right statistical test is akin to crafting a masterpiece. It requires creativity, precision, and a keen eye for detail. Let yourself be advised by a statistics professional!