Question

Use your own words to describe the general steps necessary to answer a research question using biostatistics. Use an example to illustrate your described steps

Answer 1

There is an expanding recognition with the standards of proof based drug in the careful network. As specialists turn out to be more mindful of the pecking order of proof, evaluations of proposals and the standards of basic examination, they build up an expanding nature with research outline.

RESEARCH QUESTION :-

Interest in a particular topic usually begins the research process, but it is the familiarity with the subject that helps define an appropriate research question for a study.

1 Questions then arise out of a perceived knowledge deficit within a subject area or field of study

.2 Indeed, Haynes suggests that it is important to know “where the boundary between current knowledge and ignorance lies.

”1 The challenge in developing an appropriate research question is in determining which clinical uncertainties could or should be studied and also rationalizing the need for their investigation.

These are the steps to research question using biostatstics :-

Step 1: State the Null Hypothesis:-

The invalid speculation can be thought of as the inverse of the "figure" the exploration made (in this model the scientist figures the plant stature will be distinctive for the manures). So the invalid would be that there will be no distinction among the gatherings of plants. Particularly in more measurable dialect the invalid for an ANOVA is that the methods are the equivalent. We express the Null theory as:

$${}_{}:{}_{}={}_{}=\cdots ={}_{}$$

Step 2: State the Alternative Hypothesis:-

$${}_{}:\text{treatment level means not all equal}$$

The reason we state the alternative hypothesis this way is that if the Null is rejected, there are many possibilities.

For example, ${}_{}\ne {}_{}=\cdots ={}_{}$ is one possibility, as is ${}_{}={}_{}\ne {}_{}=\cdots ={}_{}$. Many people make the mistake of stating the Alternative Hypothesis as:  ${}_{}\ne {}_{}\ne \cdots \ne {}_{}$ which says that every mean differs from every other mean. This is a possibility, but only one of many possibilities. To cover all alternative outcomes, we resort to a verbal statement of ‘not all equal’ and then follow up with mean comparisons to find out where differences among means exist. In our example, this means that fertilizer 1 may result in plants that are really tall, but fertilizers 2, 3 and the plants with no fertilizers don't differ from one another. A simpler way of thinking about this is that at least one mean is different from all others.

Step 3: Collect Data:-

Keep in mind the significance of perceiving whether information is gathered through a trial plan or observational.

Step 4: Calculate a test statistic:-

For categorical treatment level means, we use an F statistic, named after R.A. Fisher. We will explore the mechanics of computing the F statistic beginning in Lesson 2. The F value we get from the data is labeled F_calculated