Statistical tests

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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/

Sample Solution

At first look, the Trump administration’s new National Cyber Strategy may appear to be similar to its predecessors: the Obama administration’s 2009 Cyberspace Policy Review and George W. Bush’s 2003 National Strategy to Secure Cyberspace. All three texts stress the necessity of hardening vital infrastructure, collaborating with the business sector, safeguarding government networks, and forming stronger relationships to share information about online threats. Despite its resemblance to prior administrations’ goals, the National Cyber Strategy signals an abrupt and risky departure in how the US government interacts with adversaries online. Rather than concentrating on improving defensive technologies and capabilities,

oses. Here is how Krave performs with respect to its competitors’ products comprising Nestle Nesquik, Nestle Choco Shreddies, Tesco Choco Pillows and Weetabix Chocolate. (Figure 4) (Media, 2020). Figure 4 An interesting side note, we observed that Tesco, while a major market retailer for distribution of Kellogg’s Krave also houses a product that is near equal in terms of taste and design of product called Tesco’s Choco Pillows. Even the price is 50 % of the Krave brand. While there was insufficient data to find the user consumption in the UK, it is important to point out possible in-store competition within Tesco Stores for Krave. Krave is distributed in the UK, US, France, Germany, Ireland, Italy and India. It was introduced in the UK and Ireland in 2010. It was introduced in France and Germany as ‘Tresor’ in 2010. It was introduced in the US in 2012. It was introduced in India as ‘Kellogg’s Choco Fills” in 2017. Figure 5 Figure 5 is a graph about the penetration of Krave across various markets. An interesting point to note was that even within the European market, Krave was very popular in France. A possible approach was to discern the reasons behind this and deconstruct what we could adapt for the UK/I market. Consumer Trends A significant impact of Covid 19 on the breakfast cereal industry within the UK are a diversification of consumer consumption patterns into two broad categories. One segment has prioritized healthier breakfasts (Ex. Kellogg’s Fruit & Fibre) while the other has doubled down on more indulgent and unhealthier options for breakfast (Ex. Kellogg’s Crunchy Nut). (Weinbren, 2020). Some significant statistics from a 2019 survey among the UK population shows their current attitudes towards food. (Statista, 2019) 1. 27.48% of respondents prioritize the nutritional information while 26.79% prioritize the ingredients involved in their cereals. 2. 40.42% of respondents eat a healthy and balanced diet “very often”, while 10.1% eat it “always”, thus showing a huge preference for healthy food. Also, on looking at the data supplied by Kellogg’s on Krave (Figure 6) we segmented the current Krave consumers into the following. Table 1 Precautions: Though children cover a huge part of the market, we have to take major precautions to not advertise to children below 12 years as it violates Kellogg’s policies. In essence, the major players who influence the purchasing of Krave cereals is teenagers (15+) and parents (20 – 34). Further on we shall be designing our marketing efforts to increase purchase behaviour among both these segments.

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