Statistics: Totals not representative of individual sums

In summary, the speaker is struggling with analyzing census data provided by the government. The data is organized in rows and columns, with one row containing the total population and the breakdown of visible minorities and non-minorities. However, the total population does not match the sum of visible minorities and non-minorities, causing a discrepancy of +/- 10. The speaker is unsure how to proceed with calculating percentages for each area due to this inaccuracy.
  • #1
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Hi there,
This is going to be difficult for me to explain, so I will try my best. My statistics is kind of rusty...
I've been given census data to analyze and I'm having problems. The totals are all given by the government.

For a specific attribute I've been given rows of different areas, and columns pertaining to the attribute. For ex. Race is divided into visible minority (further broken down into 5 other descriptive minorities) and non-minority.
The first row contains the total population, and total number of non-minorities, and each race and then the non-minorities. This all works out fine.

However, for each row (or area) the SUM total population is off by +/- 10.
Non-minorities + Visible minorities do not equal the total, and are either above or below by 10.
I'm trying to get percentages for each area, but if the total isn't accurate...what exactly can I do statistically to make this valid?

ex) Visible Minority = VM, non-minority = NM, Race1 = n, Race2 = k, race3= l

VM = n + k + l
VM + NM = total population
But in this case VM + NM = +/- 10 the given total

If you need further examples please let me know, I've tried my best!

Thanks a lot.
 
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  • #2
I suggest you give a specific numerical example of the data layout. If the real data is complicated, you might link to an example of it if it is public data and online. Or you might make up a simplified example.
 

1. What does it mean when totals are not representative of individual sums in statistics?

In statistics, when totals are not representative of individual sums, it means that the overall sum or average of a group of data does not accurately reflect the individual data points in that group. This can happen when there are extreme outliers or when the data is skewed towards one end of the spectrum.

2. How does this affect the reliability of statistical analysis?

This can greatly affect the reliability of statistical analysis as it may lead to misleading conclusions or incorrect interpretations of the data. It is important to carefully examine the individual data points and not just rely on the overall totals or averages when conducting statistical analysis.

3. What are some common causes of totals not being representative of individual sums in statistics?

Some common causes include outliers, skewed data, small sample sizes, and selection bias. Outliers are data points that are significantly different from the rest of the data, and can greatly impact the overall totals. Skewed data occurs when the majority of the data falls towards one end of the spectrum, making the average or total not representative of the entire dataset.

4. How can this issue be addressed in statistical analysis?

This issue can be addressed by conducting further analysis on the individual data points, such as examining the distribution of the data or identifying and removing outliers. It is also important to use appropriate statistical tests and methods that are not heavily influenced by extreme values or skewed data.

5. Are there any measures that can be taken to prevent this issue from occurring?

To prevent this issue from occurring, it is important to carefully design the study or experiment and ensure a representative sample size that includes a diverse range of data points. Additionally, using robust statistical methods and conducting sensitivity analyses can help identify and address any potential issues with the data before drawing conclusions from the overall totals.

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