How Statistics Is Important For The Quality Of Data

Statistics provides us with very useful tools and techniques that aide us in dealing with real world scenarios. I have been able to learn several useful concepts by studying statistics that can aide me in making rational and informed decisions that are supported by the analysis results. Statistics as a discipline is the application and development of various processes put in place to gather, interpret, and analyse the information. The quantification of biological, social, and scientific phenomenons, design and analysis of experiments and surveys, and application of

• Provide at least two examples or problem situations in which statistics was used or could be used.

1. Statistics can be defined as the collection presentation and interpretation of numerical data.- Croxton and Crowed.

In our textbook, Stephen Lucas (2012) writes, “Darrell Huff exploded the notion that numbers don’t lie. Strictly speaking, they don’t. But they can be easily manipulated and distorted” (Lucas, 2012, p. 148). He then gives examples of how easily statistics can be used to deceive instead of inform. Statements that are made can have some truth to them. However, they tend to omit certain specifics that could persuade one to believe that it is the truth, the whole truth and nothing but the truth. Statistics can be used to mislead invalidating a theory or to support an opinion.

There are ten things stats is very important for such as: weather forecasts, emergency preparedness, predicting diseases, medical studies, genetics, political campaigns, insurance, consumer goods, quality testing, and the stock market. Weather forecasts use stats to predict the weather using prior conditions, the weather forecasts tells us how to prepare for the day and what to expect throughout the day. Emergency teams use the weather forecasts to tell them to be ready or not to help people they use statistics to tell them when danger may occur. When predicting disease the lady or man telling you about the deaths or the disease is spreading it may not

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

Faulty statistics seem to create a life of their own, they linger around. For scholarly reasons, a graduate student had come across and used a cited, nevertheless negative social statistic. This led to the questioning of where did this statistic originate from? There were no questions or critical thinking when this incorrect idea was not only formed, yet publicized. Unfortunately, society is likely to welcome and reproduce bad statistics, whether they be created or guessed, they could alter the knowledge of humanity. Data is needed though utmost importantly, need to be reputable for us to correctly and informative. Presenting many examples, explaining why statistics are essential, who uses them, and why, the writer provides somewhat of a list of considerations to make when one is approaching them.

As we have learnt, statistics is one of the most useful subjects in modern life. Newspapers and internet resources are filled with statistical data. Anyone who is unfamiliar of statistical information is at risk of being seriously misled about important real-life decisions. Knowing a little about statistics will help one to make more informed decisions about these and other important questions.

Statistics is defined as collecting,summarizing,organizing and analyzing the data. For instance, astro-statistics deals with statistical study and analysis

The history of statistics can be said to start around 1749 although, over time, there have been changes to the interpretation of the word statistics. By the 18th century, the term "statistics" designated the systematic collection ofdemographic and economic data by states. In the early 19th century, the meaning of "statistics" broadened to include the discipline concerned with the collection, summary, and analysis of data. Today statistics is widely employed in government, business, and all the sciences. Electronic computers have expedited statistical computation, and have allowed statisticians to develop "computer-intensive" methods.

Franklin and Mewborn noted that many teachers often teach students how to collect and analyze data, but haven’t taught students how to formulate questions to help them understand why statistics are so meaningful in the real-world. The authors created this

The background of statistical evidence helps in highlighting how the statistics were developed, as well as the factors taken into consideration. This influences an assessment on whether the statistical evidence is applicable to the problem under investigation. In addition, it is also important to consider the scope of the statistical evidence, which involves defining the elements and factors considered when collecting the evidence. For instance, statistics on terrorism can have different implications depending on the definition of terrorism adopted, which is within the scope. Understanding the scope of statistics helps in proving its applicability and viability, which minimizes cases of statistical deception. In most cases, statistics appear deceptive because they are not supported by enough background information (Browne & Keeley,

Statistics is a comparison of two numbers, one from a current standpoint and one from a previous time frame. In the real world, there are many ways to measure observations or responses using a mathematical analysis. By collecting this information, analyzing the data and then summarizing it with the use of numbers helps people, businesses and consumers drive decisions. There are many ways to collect statistics through surveys and other data collections. An example of this is when consumers shop, they often swipe their loyalty cards and provide stores information on items purchased and consumer shopping patterns.

There is an old adage that many mathematicians often use: "There are lies, there are damn lies, and then there are statistics." While seemingly comical, having a notion like this is indicative of the idea that statistics are not as completely objective as many people believe. The purpose of statistical analysis is to describe and summarize information, to make predictions or generalizations, and to identify relationships and associations between variables. The two major types of statistics are descriptive and inferential. Descriptive statistics describes the data by summarizing into more understandable terms; inferential statistics make predictions about variables or a population based on observable trends. However, statistics are only as good as the numbers used in the formulas, and the presentation. For instance, if a study says product A is 25% better than product B, one might ask 25% better than what? Or 80% of all physicians use Product A is that 80% of those asked, 80% of all, or 80% of what? In American Earth, there is a notation in which the engineer explains, "When this road is built you'll get ten, twenty, thirty times as many tourists as you get now" (AE, 418). How is this proven? How many tourists travel there now, and where is the data that shows that a new road would increase tourism rates by 10-30 times, meaning 100-300%?

Aron, E. Aron, & Coups, 2009). Additionally, when using inferential statistics the inferences about the research study exceeds the numbers collected in the study (A. Aron, E. Aron, & Coups, 2009). Therefore, the inferences communicate exactly how reliable data collected for the research study is. The inferences in inferential statistics also convey how significant the information collected is. The information researchers gather for their study only shows a sampling of the group of participants that the researchers use. This sampling used must vary enough to be representative of the reliability and significance of the research study (“Research Methods,” n.d.). There also must not be much variation in the data (scores) obtained for the study. When using inferential statistics the researcher must prove that the results of the study are not based on chance or the outcome leads to chance (“Research Methods,” n.d.). Ideally, if the result is a probability, the researcher’s observation of the difference would be statistically significant (“Research Methods,” n.d.). The difference would be the difference in the variables used in the study. Researchers must use descriptive statistics to establish a research study that is statistically significant. The use of descriptive statistics provides