Explain how the ANOVA technique avoids the problem of the inflated probability of

Week 6 Explain how the ANOVA technique avoids the problem of the inflated probability of making Type I error that would arise using the alternative method of comparing groups two at a time using the t­test for independent groups.  The ANOVA helps researcher avoids the problem of the inflated probability of making the Type 1 error. When more than one experiment is conducted then the risk of making type 1 errors is greater. Researcher evaluate multiple groups at the same time, which means each time an experiment is conducted there maybe a chance of falling into type 1 error that will cause inflated probability. The ANOVA helps researcher not make the error by using a­ levels. A­levels help minimize the type 1 error. Grand Canyon University. (2017). Lecture 6. Retrieved from PSY520-L6. pdf Thank you, for your post. I found myself watching the videos a couple of times to try and gain a better understanding for the one –way and two- way factors. Two classmates I noticed used list to show the difference between both factors and it was awesome. One an example I found to be very helpful for me to understand one way factor was (determining the difference in the mean height of stalks or different types of seeds, because there is more than one mean you can use the one way ANOVA since there is only one factor making the height different) I agree with your entire post and love how you shared the following information. Explain the major differences between analyzing a one­way ANOVA versus a two­factor ANOVA, and explain why factorial designs with two or more independent variables (or factors) can become very difficult to interpret. The major differences between one ­way and two factors ANOVA is ANOVA is known as a tool use for analysis important research, to help allow researcher make comparisons within two or more population and assist researcher during test (Witte and Witte 2015). One ­way and two factors ANOVA have major differences, one way ANOVA are known for having a hypothesis test, test the differences in the population means based on the characteristic or the factor of a to b. Researcher using one­way ANOVA would like to gather information for one variable to see if it’s near the basic mean. An example of One­ way ANOVA can be used to determine the difference in the mean height of stalks or different types of seeds, because there is more than one mean you can use the one way ANOVA since there is only one factor making the height different. As for Two – way ANOVA the hypothesis test, test between population based on the multiple characteristics from a to c and from b to c. Two-way ANOVA is better than one-path ANOVA as the strategy has certain points of interest more than one- way ANOVA (Analysis of variance (ANOVA), 1998). When using two-way ANOVA you may have three different types of seeds and the possibility that four different types of fertilizer to use but then you would want to use a two way ANOVA to determine