Hier erfahren Sie, wie Sie logisches Denken anwenden können, um Muster und Trends in Forschungsdaten aufzudecken.

Sometimes we use data with the aim of finding an answer that we already have internalized, and that's what we seek... Maintaining an open mindset is very important because often we start with our own biases and a more or less clear position. A procedure that includes flexibility often grants us discoveries and new perspectives on the data, allowing us to consider different approaches that arise during data analysis.

Research is about stating an hypothesis, and then designing and carrying out experiments to adjudicate the results. In the end analysis, it is about finding patterns that are consistent and robust. Sometimes you don’t see what you aren’t looking for, so staying flexible in your interpretation is key. Some results are idiosyncratic, which is why it is important to have a large enough data set so that your conclusions are not spurious. Using graphical tools can be extremely helpful to ascertain patterns that are not obvious by starring at the data alone.

Spotting patterns in research data involves: - Identifying recurring themes, trends, or anomalies. - Comparing datasets to discern variations over time. - Categorizing information based on shared characteristics. - Seeking underlying principles or relationships. - Applying logical reasoning to interpret findings accurately.

It's important to start with an well defined hypothesis, and then design a study to try to answer that question. The hypothesis drive what study designs are appropriate, so it's very important to spend the time needed to get your hypothesis correct! Once you have run your study and collected the data, always look at some basic graphs of it (your endpoint vs. each of the 'explanatory variables') and look for any kind of patterns or trends. Then, for every pattern/trend you see, ask yourself "Does that make sense?" And finally, don't automatically ignore an outlier (unless you know something happened during it's collection that would affect the quality of that data point). Sometimes the outliers contain the most important information!

We need to collect data to analyze it, check for consistency, detect biases, and determine the trends it reflects. Scraping, for example, allows us to gather data from the web. One of the tools used for this in Python is Scrapy. Then we can use Pandas and Tableau, for instance.

There are well-known and widely used tools that make our work easier in various areas. For creating diagrams, for instance, we have Microsoft Visio, Draw.io, and Creately, which are very comprehensive and user-friendly options. In data analysis, the most commonly used languages are Python and R. Python is more widely used and has libraries like Pandas and Polars that allow for data manipulation and analysis. For data visualization, tools like Tableau and QlikView are popular for creating charts and dashboards.

This is the most challenging and interesting part of the process. Let's say you are looking for how a drug works, its mechanism of action. You have to design experiments that address as many possible ways of working and eliminate ways as you experiment. In addition, the drug may have multiple ways of working or the drug is metabolized to a different active compound. Just because the drug binds to a site in the test tube, does not mean it does so in the body, So studies have to include "in vivo" studies as well. As new findings are found and published, new experiments can be designed. In science, its an ever evolving process and we get closer and closer to understanding as we continue to ask questions and test them in a logical manner.

To conduct a successful and objective reflection, we must first use web scraping techniques to gather data from the web, and then analyze this data. However, after the analysis, it's important to step back and review the results from different perspectives, compare the data, verify its coherence, and consider possible interpretations of this data.