## Issues of Analysis

**Types of data**

Think about any collected data that you have experience of; for example, weight, sex, ethnicity, job grade, and consider their different attributes. These variables can be described as categorical or quantitative.

**Statistical hypothesis testing****Null and alternative hypotheses
**The formal statistical procedure for performing a hypothesis test is to state two hypotheses and to use an appropriate statistical test to reject one of the hypotheses and therefore accept (or fail to reject) the other.

**One- and two-tailed tests
**Also known as one- and two-sided tests.

**The p-value (level of significance)**

All statistical tests produce a p-value and this is equal to the probability of obtaining the observed difference, or one more extreme, if the null hypothesis is true. To put it another way - if the null hypothesis is true, the p-value is the probability of obtaining a difference at least as large as that observed due to sampling variation.

**Statistical power**

The use of a significance level of 5% controls the probability of erroneously rejecting the null hypothesis when it is, in fact, true. Rejecting the null hypothesis when it is true is called a **Type I error**. However, there is another error that can be made - failing to reject the null hypothesis when it is, in fact, not true. This is called a **Type II error**.

**Validity and Reliability **

Validity and Reliability are the key characteristics in quantitative research that reflect quality and rigour in design. A well written research paper will indicate how validity and reliability have been assessed.

There are similar issues within qualitative research, as per Lincoln & Guba (1985)

**Validity
**refers to the accuracy and truth of the data and findings that are produced. It refers to the concepts that are being investigated; the people or objects that are being studied; the methods by which data are collected; and the findings that are produced. There are several different types of validity which all contribute to the overall credibility of the research.

**Reliability
**is concerned with the consistency and dependability of a measuring instrument, i.e. it is an indication of the degree to which it gives the same answers over time, across similar groups and irrespective of who administers it. A reliable measuring instrument will always give the same result on different occasions assuming that what is being measured has not changed during the intervening period