Hypothesis testing is a statistical procedure in quantitative research used to test the plausibility of a claim using numeric data. The purpose of hypothesis testing is to test whether the null hypothesis (there is no difference, no effect) is rejectable or approvable. If the null hypothesis is rejectable, then the research hypothesis is acceptable. If the null hypothesis is acceptable, then the research hypothesis is rejectable. In hypothesis testing, a value is set to assess whether the null hypothesis is acceptable or rejectable and whether the result is statistically significant. Hypothesis testing has 6 steps;
Hypothesis Testing Procedure
- Restate the research question as research hypothesis and a null hypothesis about the populations.
- Determine the characteristics of the comparison distribution.
- Determine the cut off sample score on the comparison distribution at which the null hypothesis should be rejectable.
- Determine your sample’s score on the comparison distribution.
- Decide whether to reject the null hypothesis.
- Make inferences and draw a conclusion for your research question.
Many students and researchers, particularly novices, lack knowledge in Statistics which makes the hypothesis testing procedure challenging. However, this should not be a stumbling block for you, Statistics Heroes got you back. Our experts will conduct hypothesis testing steps for your assignments, homework, classwork, projects, dissertations and thesis. We are experts in statistical tools used for hypothesis testing such as SPSS, R, Excel, JASP, STATA, Minitab, and Jamovi.
We use both parametric and non-parametric tests in hypothesis testing. However, this depends on the assumptions made regarding the sampled populations. Parametric tests that we will help you with are t-tests (Paired & Independent samples), ANOVA, Pearson correlation and linear regressions. Non-parametric tests applicable in hypothesis testing include Wilcoxon rank-sum test, Kruskal-Wallis test, Mann-Whitney U test and Spearman correlation. Other tests that our experts will help you with are Discriminant analysis, cluster analysis, survival analysis, and principal component analysis.