Basic Statistics

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Basic Statistics

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Basic Statistics- means of collecting, analyzing, interpreting and presenting data and findings
1. Analyzing data often requiring organizing it
  1. Mean- average of all values
  2. Median-the value at the midpoint of data collected
  3. Mode- the value that occurs most often
  4. Range- difference between lowest and highest vaules
2. Data Categorized in different ways
  1. Quantitative- collected in some numerical form (counts, mass, time)
  2. Qualitative- collected in a non-numerical (quality) form (color, texture, smell)
  3. Ordinal- collected with some order or pattern (ranking system)
  4. Nominal- collected with no specific order or pattern
3. Regression- is the measure of the relationship between two variables
  1. relationship between x and y
  2. cause or correlation?
4. Interpreting Data on a graph/chart
  1. must analyze carefully
    1. look for titles, labels, key, scale

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Hello today we are going to discuss the basics of statistics.

So I thought we could start with this little cartoon that shows a bar graph comparing 4 different things labelled A, B, C, D and often times bar graphs are used to display averages. But this graph also includes some additional statistics know as error bars and data bar B is being judged harshly because of its large margin of error….. making it less trustworthy in what we are seeing in the average. Kind of like if you wanted to find out the average height of American males and then find out there are some NBA players in that mix…. your average might reflect one picture of the average height, but the error bars would show the deviation of the height of those sampled individuals helping you understand and compare your data more clearly.

So statistics begins from the very beginning of research in that one needs to know exactly how data is going to be collected, whether it is data actually acquired by the individual directly or indirect secondary research… one has to be concerned with how the data was obtained and screen for good practices that are without bias. And once data is obtained much of statistics is putting that data through the ringer to see how it flushes out. Only to interpret what the data might be telling us and then compiling it in such a way that we can share our findings with others for peer review and further research. So it is not uncommon for numerical data to be organized in such a way to find the mean or average, the median or midpoint values the mode that number we see time and time again and our high and low values differences or the range. This meme has been circulating around social media labeling the guy looking at the other girl as being average. That which supposedly caught his eye is said to be an outlier and original girlfriend is the median. I hope you can take that with a grain of salt. Agree or disagree it is meant to help understand those terms better.

And of course Data can be categorized in different ways. Quannnnnnntitative data (and I am emphasizing the n on purpose – is data collected in some numerical form (counts, mass, time) Qualitative- collected in a non-numerical (quality) form (color, texture, smell). Ordinal data- is data that is collected with some order or pattern (ranking system) and Nominal- is data collected with no specific order or pattern in mind.

Once data is collected it is typically put into some graph to better visualize and understand the relationship between two variables. So you can see here a scatter plot and then a trend line added showing the relationship that as the x variable increases, so too does the y (a positive correlation. Another name for a trend line is a line of regression. Spreadsheets are excellent for create these. And from that interpretation we can sometimes show correlation between those two variables, however correlation does not necessarily extrapolate to causation. S we have two trends in this graph on the left, The divorce rate in Maine over time follows a very close pattern to the consumption of margarine. This does not mean that one caused the other. they correlate but don’t necessarily causate. Something we have to becarefull we don’t do, or even allow others to fool us with.

And to not be duped, one of the most important things we need to do when interpreting data on a graph is to carefully read the title (which should tell us what variables are being compared. Look for labels on the axes or charts, what are the units of the data being measured, how is the data being scaled and is their a key to identify specifics about the data.

So in review….. statistics helps us best to understand and evaluate what the data means. First we must be assured the data is collected and organized. There are different categories of data that can be collected (numerical, quality, orderly or without specific order. As we graph and evaluate data we can look for trends (line of regression) and find correlation and sometimes even make causation (but be careful) and of course we always want to scrutinize our own data as well as those presented to us by others to make sure we know exactly what we are looking at to best infer what it may mean.

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