Understanding Business Research

Each statistic in the descriptive form lowers the quantity of data into a much simpler summary.

Descriptive statistics are digits that are used to summarize and describe a given range of data (Klenke, 2008). Basic descriptive data includes, mean, median, mode, variance and standard deviation. The data can be rearranged in an ascending order as follows:

Validity refers to whether the research conducted is what it intended to be. Validity involves dependability, which means, a valid measure must be reliable. But, reliability doesn’t have to link to validity, a reliable measure is not required to be valid.

The last few weeks we covered descriptive statistic: the central tendency, variability, correlation and Z-score. Today’s session is a little bit different, we will be talking about statistical significance. Statistical significance is the level of risk one is willing to take to reject or accept a null hypothesis while it is true and it separate random error from systematic error. When doing a study or research, the statistical significance shows that the difference obtained were not caused by chance. Inferential statistics, the T-test, partition noise from bias by studying a random sample than the population in which we are interested and from the results we infer. The advantage of using sample than a population, it is convenient, saves time, energy and money because n is smaller than population and above all it helps to control systematic and random errors. When we are making a conclusion, we should have a certain confidence or probability of being right and that is called the alpha level; which the risk you are willing to

The mean (x) is a measure of _central__________ __tendency___________ of a distribution while the SD is a measure of __dispersion___________ of its scores. Both x and SD are _descriptive___________ statistics.

In this paper Team C has discussed the issue of poor employee retention concluding in a high employee turnover rate. This is an issue that can be common among some companies and that is a great example of

Descriptive Statistics Nineteenth-century British statesman Benjamin Disraeli once said, “there are three kinds of lies: lies, damned lies and statistics”. When he made this statement, he was speaking on the fact that statistics can come out skewed or if not properly analyzed can be slightly falsified without anyone truly knowing.

Descriptive statistics are brief descriptive coefficients that summarize a given data set, which can be either a representation of the entire population or a sample of it. Descriptive statistics are broken down into measures of central tendency and

Validity is known as the quality or correctness of what is being measured and reliability is

Statistics is a mathematical science pertaining to the collection, analysis, interpretation or explanation, and presentation of data. It is applicable to a wide variety of academic disciplines, from the physical and social sciences to the humanities. Statistics are also used for making informed decisions – and misused for other reasons – in all areas of business and government. Statistical methods can be used to summarize or describe a collection of data; this is called descriptive statistics. In addition, patterns in the data may be modeled in a way that accounts for randomness and uncertainty in the observations, and then used to draw inferences about the process or population being studied; this is called inferential statistics. Both

Descriptive research is conducted primarily to collect data about variables rather than to test hypotheses about the relationships between variables. In another word a descriptive study is conducted to describe ‘how things are’.

The easiest way to test for reliability is through test-retest method. For instance, measuring the weight of students, if the weight the same after two different tests using a similar machine then the measurement is reliable. Validity, on the other hand, refers to the accuracy of a test or measurement. For instance, in calculating the mean of students, the method used need to be correct to give the correct results. The results will be said to be valid. A measure can be reliable but not valid because reliability focuses merely on consistency while validity focuses more on accuracy (Babbie,

Reliability and validity are two different concepts. Reliability refers to the extent assessments are consistent. For example, if a student took an exam multiple times and received a low score each time, even though he performed very well on the exam, the exam is reliable. It produced the same low score each time, thus showing consistency. However, just because an exam is reliable, this does not mean that an exam is valid. Validity refers to the accuracy of the assessment. In this example, the student performed very well on the exam, yet he received a low score. In reality, if a student performs well, he should receive a high score. These two procedural tools are used and considered when creating standardized testing.

B) i: The purpose of using descriptive statistics is to try to estimate the student population’s stress level as they are preparing for exams. ii: The purpose of inferential statistics is to try to make sure that the population of students are not too stressed for their exams.

Before we continue the discussion about the basic descriptive data elements that are needed to answer the question I previously posed, it is important to understand what we mean by descriptive statistics. Descriptive statistics describe the features of the data collected. In other words, descriptive statistics describe “what” the data looks like, but it does not tell you why or how data elements interact or influence one another. Descriptive statistics provide a defender with summaries about the data collected and these summaries may be either quantitative or visual. Quantitative descriptive statistics are the sum of data points that are usually reported as total numbers or averages in a report. For example, defender offices can use descriptive statistics to determine the total of open felony cases per attorney in one year or say that on average they had x number of open cases in the last two years. These are considered to be summaries of caseloads that describe the data collected but it does not tell you if a certain variable (number of cases opened in this example) has an influence (or is correlated) with the final outcome of a case. When we say that a variable is correlated we mean that the knowledge of a certain variable (caseload numbers) can allow you to consistently predict the action of another (final dispositional outcomes). For example, if you mix two hydrogen and one oxygen (H20), you get water. We know that every time you have the interaction of these