Scientific Analysis
Did you ever wonder how samples of data are analyzed in order to make valid scientific claims that the Earth is warming? Once temperature datasets are collected in digital format, comparative analysis can be applied. Comparative analysis is a type of analysis that compares one or more datasets to determine their consistency with one another.
If a consistency is found between the datasets, then it adds support to a scientific claim. Such techniques can involve a statistical test called correlation, which is a quantitative figure measuring the interdependence of variables. This figure contains a quantity between 0 and 1. The closer the correlation is to 1 between two datasets, the stronger their interdependence.
Comparative analysis also includes a technique to visually inspect the data. This is accomplished by constructing a time series, which is a series of connected values at certain points through time on a graph. By visually observing the trends, which are measures of the change in quantities through time, in the time series, you can determine if the datasets have a consistency or not.
In the next section, we discuss what major datasets are used in comparative analysis to monitor changes in the Earth's temperature. Whenever someone debates climate change, they are referring to these datasets.
Datasets Used in Global Temperature
These days, anyone with a computer can download scientific data and graph data points for a comparative analysis. Using the example of the Earth's temperature, we first need to know the datasets used in the study of the Earth's global temperature. When we see that a dataset doesn't agree with the others, it can be difficult to interpret our findings and identify the sources of error involved.
When discussing climate change, there are three scientific datasets that refer to surface temperature estimates:
- HadCRU (Hadley Centre/Climate Research Unit)
- NOAA (National Oceanographic Atmospheric Administration)
- NASA GISS (Goddard Institute for Space Studies)
Analysis of Earth's Temperature
Now that you're familiar with the different datasets used to study the temperature of the Earth, we will see how to apply comparative analysis on them. Once they are downloaded digitally, the datasets are graphed on a computer with connecting lines that go through their data points. These three datasets do not span over the same time period. The HadCRU begins in 1850 while the NOAA and NASA GISS start in 1880.
Once these datasets are graphed separately, you can visually inspect if they agree through time. Generally, these three datasets agree well with only minor differences in certain years of their time series. To make a more detailed comparative analysis, the correlation as described can be applied to quantify their interdependence and consistency. A high value closer to one for this quantity would support the claim that these datasets are valid for usage.
Lesson Summary
All right, let's now take a moment or two to review. As we learned, comparative analysis is a type of statistical method whereby two or more datasets are compared to determine their consistency with one another. It can also validate a scientific investigation or hypothesis that needs to be tested.
A common statistical test within this type of data analysis to measure the interdependence of the datasets is called the correlation, which is a quantitative figure measuring the interdependence of variables, or a statistical value between 0 and 1.
Another test involves visually inspecting a time series, which is a series of connected values at certain points through time on a graph. By looking at the graph, we can look at trends, which are measures of the change in quantities through time in order to determine if there is a consistency between the datasets through time.
In our example, we described three major datasets used in studying climate warming. They are the HadCRU, NOAA, and NASA GISS datasets. They have been constructed in a time series and all have been found similar without any major discrepancies.
