## Question: Datasets For Multiple Hypothesis Testing

### Data Science- Hypothesis Testing Using Minitab and R

If you have measured individuals (or any other type of "object") in a study and want to understand differences (or any other type of effect), you can simply summarize the data you have collected. For example, if Sarah and Mike wanted to know which teaching method was the best, they could simply compare the performance achieved by the two groups of students – the group of students that took lectures and seminar classes, and the group of students that took lectures by themselves – and conclude that the best method was the teaching method which resulted in the highest performance. However, this is generally of only limited appeal because the conclusions could only apply to students in this study. However, if those students were representative of all statistics students on a graduate management degree, the study would have wider appeal.

### SOLUTION: Use Hypothesis Testing and the data - …

When we get the data we will calculate Z and then look it up in the Z table to see how unusual the obtained sample's mean is, if the null hypothesis Ho is true.

That is, in the practice of statistics, if the evidence (data) we collected is unlikely in light of the initial assumption, then we **reject** our initial assumption. One place where you can consistently see the general idea of hypothesis testing in action is in criminal trials held in the United States.

## Solution-Formulate and test a hypothesis using the data

Big data is anything but out of the box. This is a disruptive technology without packaged solutions. Sure, you can acquire big data technology, but without understanding and hypothesizing how previously hidden data can be harvested and applied to business processes, challenges or opportunities, big data becomes another shelfware solution with a disappointing payback and short lifespan.

## Data Science- Hypothesis Testing Using Minitab and R …

Generally, when comparing or contrasting groups (samples), the null hypothesis is that the *difference between means (averages) = 0*. For categorical data shown on a contingency table, the null hypothesis is that any differences between the observed frequencies (counts in categories) and expected frequencies are due to chance.