300 words response:

Over the course of this class, we’ve learned about different types of ways to analyze data presented to us. Each of the tools presented to us give us different perspectives about our data.

One thing that is important to keep in mind with all of this is that choosing a technique for analyzing a given data set is often dependent on the question or questions being asked. In addition to examining the question and what we want to accomplish in getting an answer, it is important to consider the type of data we’re working with. This is more specifically referring to examining the variables that are in the problem, whether they are categorical, ordinal, or interval and then if the data set itself is normally distributed or not (UCLA, n.d.). Asking these sorts of questions and analyzing our initial data with this lens allows us to understand the general direction that needs to be pursued in order to decide on a statistical test.

Having said that, statistical tests like the t-test, chi square test, etc. can specifically help predict patterns based on what kinds of comparisons between groups are trying to be made. These are more complex concepts of what we can accomplish, but even if we go back to the simplest forms of analyzing data, such as analyzing trends in central tendency, can also provide insight into changes occurring over time in a data set (Emerald Group Publishing, n.d.).

Beyond making sure that we are using the best methods of analyzing the data through statistical testing or simply data observation, it is also important to question how effective certain methods are in accomplishing the goal of data analysis and if they are the easiest way to go about doing the same. This is where analysis of descriptive data also can be used to our advantage, with the eventual accompaniment of a statistical test such as an ANOVA or t-tests. This combination of observation and statistical analysis can then be used to test whether the differences are due to chance or not.

From our discussion here, we can observe that the combination of data observation and statistical analysis go hand in hand and are the best means for transforming information into useful, understandable knowledge. Understanding data doesn’t have to mean using the most complicated testing, or the simplest for that matter but using a combination of the two can truly aid in making sense of data provided to us and making the values something to conceptualize, whether it be in the context of a real world problem or a word problem.

References 

Emerald Group Publishing. (n.d.). Choose the right statistical technique. Emerald Publishing. Retrieved March 10, 2022, from https://www.emeraldgrouppublishing.com/how-to/research/data-analysis/choose-right-statistical-technique#basic-techniques

UCLA. (n.d.). What statistical analysis should I use? UCLA Advanced Research Computing. Retrieved March 10, 2022, from https://stats.oarc.ucla.edu/spss/whatstat/what-statistical-analysis-should-i-usestatistical-analyses-using-spss/

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