Data analytics involves sorting and organizing raw data to extract useful insights. It is important to approach data analysis carefully and think critically about the conclusions drawn. Trends can emerge during data organization and can be highlighted through data writing. Graphs, charts, and text-based data writing are all forms of data analysis. It is important to view summarized data and conclusions critically, considering factors such as the data source and sampling method. Reputable researchers will provide information about data collection techniques, funding source, and point of data collection.
Data analytics is a practice where raw data is sorted and organized so that useful insights can be extracted from it. The process of organizing and thinking about data is key to understanding what data does and does not contain. There are various ways people can approach data analysis and it is notoriously easy to manipulate data during the analysis phase to draw some conclusions or agendas. For this reason, it is important to be careful when data analysis is presented and to think critically about the data and the conclusions drawn.
Raw data can take a variety of forms, including measurements, survey responses, and observations. In its raw form, this information can be incredibly useful, but also overwhelming. In the course of the data analysis process, the raw data is sorted in a way that will be useful. For example, poll results can be tallied, so people can see at a glance how many people answered your poll and how people answered specific questions.
In the course of organizing data, trends often emerge, and these trends can be highlighted in data writing to ensure readers take notice. In a random survey of ice cream preferences, for example, more women than men might express a fondness for chocolate, and this might be a point of interest to the researcher. Modeling data with the use of mathematics and other tools can sometimes exaggerate such points of interest in the data, making them easier for the researcher to see.
Graphs, charts, and text-based data writing are all forms of data analysis. These methods are designed to refine and distill the data so that readers can glean interesting insights without needing to sort through all the data themselves. Summarizing data is often critical to supporting arguments made with that data, as it presents it in a clear and understandable way. The raw data can also be included in the form of an addendum so people can look up the details for themselves.
When people encounter summarized data and conclusions, they need to view them critically. Asking where the data comes from is important, as is asking about the sampling method used to collect the data and sample size. If the data source appears to have a conflict of interest with the type of data being collected, this can question the results. Likewise, data collected from a small sample or a sample that isn’t truly random may be of questionable utility. Reputable researchers will always provide information about the data collection techniques used, funding source, and point of data collection at the outset of the analysis so that readers can think about this information as they review the analysis.
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