Inferential statistics compare differences between treatment groups and generalize the larger population of subjects. The study involves testing a null hypothesis or false balance. The study conducted by Cook et al. (2017) used Analysis of Variance or ANOVA type of inferential statistics to generalize the effect of misinformation. The study compares perceived consensus in the control conditions versus the condition that received misinformation. A two-way ANOVA that contains two independent variables was used to analyze results ( Kim, 2014) . The results included two-way interaction between the consensus and inoculation interventions and four dependent variables (Cook et al., 2017) . ANOVA test was conducted using the Car package for the R statistical programming environment for the six dependent variables, with the independent variable been the effect of misinformation ( Cook et al., 2017) .
ANOVA test is vital when finding results of an experiment comprising of two or more groups. ANOVA test inferential statistics was essential for this study since it generates a number used to determine the p-level of rejecting the null hypothesis ( t (284) = 2.05, p = .046) ( Cook et al., 2017) . ANOVA is essential when testing three or more variables since it leads to fewer errors appropriate for a range of issues. Inferential statistics are essential in assessing the strength of the relationship between independent variables and dependent variables. Through the use of Anova, the researcher manages to compare the means of each group and spread out the variance into diverse sources ( Kim, 2014) . ANOVA test was essential to determine whether inoculation neutralizes the influence of misinformation. Also, through Anova statistics, the study showed each dependent variable's effect on misinformation.
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Apart from ANOVA, an Analysis of co-variance will help the study arrive at accurate results. Analysis of Co-variance is a continuous inferential statistics that uses regression variables ( Kuhar, 2010) . This type of inferential statistics is essential for studying differences between the Average values of variables. For this study, an analysis of co-variance would help the researcher find the seven constructs' average value. Also, Statistical Significance (T-Test) will be useful for the study since it compares two groups' means and understands their differences.
References
Cook, J., Lewandowsky, S., & Ecker, U. K. (2017). Neutralizing misinformation through inoculation: Exposing misleading argumentation techniques reduces their influence. PloS one , 12 (5), e0175799.
Kim, H. Y. (2014). Analysis of variance (ANOVA) comparing means of more than two groups. Restorative dentistry & endodontics , 39 (1), 74-77.
Kuhar, C. W. (2010). Experimental Design: Basic Concepts.
