Why Statistics is Not Difficult

R.C’s post

Statistics the gathering of information, which can be referred to as data.  This information can be asked in survey format, discussions or written or by conducting experiments.  This information is complied, studied, using many statistical methods, and the results, also known as the outcomes are documented.  The researchers must have knowledge of what they are studying and how they want to study it.  The researchers must also identify variables that can alter the results, favor the results in one way or another or even cause harm by reporting false findings or false positives. This is most commonly used in matching pairs and this is done to determine if there is a difference in variations.  When using an analysis of variance, you will need to determine the mean of each group of variables, determine the sum of squares of error, determine the sum of squares of treatment, determine degree of freedom, determine the mean squares and determine the f factor. In order to explain in a simplified form, you would first need to understand exactly what it is meant by “Analysis of Variance” also known as ANOVA. 

 This is a statistical method used to test differences between two or more means.  Simplified, it would be determining the right method of study or research that would best fit the study and the sample or population you want to study and report out the data ( results or findings ) that have the least amount of variable or plainly said, the “best “ results.  Did “4 out of 5 dentist really choose _____ gum?”  Did they only survey 5 dentists?  Were the 5 dentist employed by the company that produces the gum?  Were the 5 dentist handpicked by the same company that produces the gum? Did the dentist actually recommend that brand or maybe they just recommended “sugarless gum “?

A.M’s post

Statistics is a very tricky subject. Most of us get scared because of its stereotyping. Because someone told us that statistics is not everyone’s cup of tea, we believe in that. There are several terms in statistics which are difficult to understand if you just look at it. Statistics is all about tests. These tests help us with research. The trick to get a good result is to pick a right test. Analysis of Variance is one of the test used in Statistics. It is also called ANOVA. Let’s take a close look at what ANOVA is?

In order to understand ANOVA, lets first take a look at other two tests used in Statistics. First one is Standard t-est. According to cbgs.k12.va.us website, “The most basic type of statistical test, for use when you are comparing the means from exactly TWO groups, such as the control group versus experimental group”. The second one is Paired t-test. This is very powerful test and it is used for detecting differences in sample before and after experiments.

What happens if you have more than two groups? The two tests which we discuss before cannot be used. If you have three or more groups, you will use a test called ANOVA (Analysis of variance). With no other variables, you use one-way ANOVA test to test three or more group. Example provided by explorable.com shows how one-way ANOVA test is used. “Suppose we want to test the effect of five different exercises. For this, we recruit 20 men and assign one type of exercise to 4 men (5 groups). Their weights are recorded after a few weeks. We may find out whether the effect of these exercises on them is significantly different or not and this may be done by comparing the weights of the 5 groups of 4 men each.

Two-Way ANOVA is another kind of ANOVA test where two or more group are tested in response to two different independent variables. These variables are also referred as factors. The purpose of a two-way ANOVA is to understand if there is an interaction between the two factors. These examples from statistics.lared.com clarifies the concept. A researcher was interested in whether an individual's interest in politics was influenced by their level of education and gender. They recruited a random sample of participants to their study and asked them about their interest in politics, which they scored from 0 to 100, with higher scores indicating a greater interest in politics. The researcher then divided the participants by gender (Male/Female) and then again by level of education (School/College/University). Therefore, the dependent variable was "interest in politics", and the two independent variables were "gender" and "education".

 K.W’s post

Interpreting results of ANOVA:

• Reported as an F statistic
• The F distribution table is used to determine the level of significance of the F statistic.
• If F statistic is = or > than the appropriate table value, there is statistically significant difference between the groups.
• If only two groups are being examined, then the location of the significance difference is clear.
• If more than two groups, it is not possible to determine from the ANOVA where the significant difference occurs.

If wanting to determine where the significant difference occurs, you will need to conduct a posthoc analyses. This will determine the location of the differences among the groups being studied (Grove, Gray, & Burns, 2015).

Posthoc tests are: Bonferroni procedure and the Newman-Keuls’, Tukey’s honestly significantly (HSD), Scheffes, and Dunnett’s tests.