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6 changes: 3 additions & 3 deletions assure-analyze/analysis.qmd
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Expand Up @@ -58,7 +58,7 @@ There are five main data analysis method types, and each has a different process
| Causal Analytics | Understand the cause and effect relationships between variables of interest. | All causal analytical tools require strong assumptions and can never fully capture all of the context of the relationships in questions (i.e. extraneous variables that cannot be measured or assessed). If these methods are used, be sure to ground-truth the results with the communities the data you're using are meant to represent. |
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### **Beware of Common Data Fallacies**
### Beware of Common Data Fallacies

As with any analysis, we need to understand the limits of our data and the methods we use so we interpret the results we find appropriately. Below is an overview of common data fallacies to be aware of and avoid as you're interpreting your results and making conclusions[^analysis-1]:

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| Regression Towards the Mean | When something happens that’s unusually good or bad, it will revert back towards the average over time. | A key component of this fallacy is that random chance influences the outcome. When looking at our data through an equity lens, we need to remember that racism and injustice are central to our collective history and can be traced back to *before* the founding of our country. We live and work in institutions and systems that have inherited those unjust decisions and processes. We know that, as government representatives, if we're not clear and intentional about advancing racial equity in the work we do, it won’t happen and we will continue to perpetuate racial inequity. In other words, because of the unjust systems in which we work, we cannot depend on this fallacy and wait for the unusually "bad" or unjust results/trends to correct themselves or revert back to more equitable trends. **Because government agencies created and perpetuated environmental racism, it is [our responsibility]{.underline} to proactively advance racial equity and justice in all the work we do.** |
| | | |
| ![](images/data-fallacy_regression-toward-mean.png) | | If our collective history and its deep connection to racism and injustice are not familiar to you - we recommend taking the Advancing Racial Equity training series offered by the [Water Boards Training Academy](https://launcher.myapps.microsoft.com/api/signin/82768508-b765-4d4e-bc76-c73262ddb0cf?tenantId=fe186a25-7d49-41e6-9941-05d2281d36c1) and reviewing the [GARE Framework: Normalize, Organize, and Operationalize](https://www.racialequityalliance.org/resources/racial-equity-toolkit-opportunity-operationalize-equity/). More details on both of these actions and others can be found on the [Getting Started](https://cawaterboarddatacenter.github.io/equity-data-handbook/get-started/) page. |
| ![](images/data-fallacy_regression-toward-mean.png) | | If our collective history and its deep connection to racism and injustice are not familiar to you - we recommend taking the Advancing Racial Equity training series offered by the [Water Boards Training Academy](https://launcher.myapps.microsoft.com/api/signin/82768508-b765-4d4e-bc76-c73262ddb0cf?tenantId=fe186a25-7d49-41e6-9941-05d2281d36c1) and reviewing the [GARE Framework: Normalize, Organize, and Operationalize](https://www.racialequityalliance.org/resources/racial-equity-toolkit-opportunity-operationalize-equity/). More details on both of these actions and others can be found on the [Getting Started](https://cawaterboarddatacenter.github.io/equity-data-handbook/get-started/) page. \| |
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| Sampling Bias | Drawing conclusions from a set of data that isn’t representative of the population you’re trying to understand. | During your [Plan and Prepare](https://cawaterboarddatacenter.github.io/equity-data-handbook/plan-prep/) you will think through the type of data that's needed to adequitely represent the populations related to your project objectives. If the data available is not adequately representative, then you may need to revise the types of questions you have of the data (and analytical methods). Or, it might be worth considering collecting the data yourself or with partners. |
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| ![](images/data-fallacy_survivorship-bias.png) | | It's important to take the time during the [Plan and Prepare](https://cawaterboarddatacenter.github.io/equity-data-handbook/plan-prep/) phase to understand what data are needed to achieve the objectives of your project, what data are actually available, and to understand what all of that means for your [Collect and Process](https://cawaterboarddatacenter.github.io/equity-data-handbook/collect-process/) phase, and how that might impact your interpretation. |
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# Resources
## Additional Resources

- College of Water Informatics [Machine Learning Handbook](https://www.waterboards.ca.gov/resources/oima/cowi/machine_learning_handbook.html)
- Flood M. D., Lemieux V. L., Varga M., and Wong B. L. W. (2016) '[The application of visual analytics to financial stability monitoring](https://doi.org/10.1016/j.jfs.2016.01.006)', Journal of Financial Stability
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2 changes: 1 addition & 1 deletion assure-analyze/qaqc.qmd
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Expand Up @@ -47,7 +47,7 @@ According to the [Water Boards Quality Assurance/Quality Control Website](https:

- If data is of insufficient quality and needs to be excluded from future steps - where and how will documentation summarize what measures/standards were used to make such determination and which data were excluded?

## **Resources**
## Additional Resources

- [Water Boards QAQC Webpage](https://www.waterboards.ca.gov/water_issues/programs/quality_assurance/)
- [SWAMP Bioaccumulation Monitoring Program Training Series](https://www.waterboards.ca.gov/water_issues/programs/swamp/bioaccumulation_monitoring/training.html)
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Expand Up @@ -378,7 +378,7 @@ When determining how you will include table(s) in your visualization or data pro

- **What do blank cells mean?** Screen readers do not tell the viewer that a cell is blank; they just skip the cell altogether. If a blank cell has a certain meaning in your data story (e.g. NA, 0, no data, etc.), state that within the cell explicitly or add a dash ( - ) to the blank cell(s) and add a note at the bottom of the table (or within the caption) to make viewers aware of the meaning of the dash.

## Resources
## Additional Resources

- Justinmind Blog (2020) [User-centered design: a beginner’s guide](https://www.justinmind.com/blog/user-centered-design/)
- [Urban Institute](https://www.urban.org/) Resources
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2 changes: 1 addition & 1 deletion background.qmd
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Expand Up @@ -24,7 +24,7 @@ Goal 1a of the Racial Equity Action Plan is to ensure Water Boards data are acce

The Racial Equity Data Action Plan is being developed by the Water Board’s Racial Equity Data Subcommittee of the [Environmental Justice Roundtable](https://cawaterboards.sharepoint.com/sites/WB-OPP-EJRoundtable "EJ Roundtable SharePoint site") which is a group of volunteer staff from across the Water Board led by the [Office of Information Management and Analysis](https://www.waterboards.ca.gov/resources/oima/) and the [Office of Public Participation](https://www.waterboards.ca.gov/about_us/public_participation/). This Handbook is intended to address Item 1 above by providing staff a best practices guide for incorporating racial equity concepts into the Water Board programs using data and information. Item 2 above will be iterative and grow as staff and programs begin to utilize the guidance and tools found within this handbook. To fulfill Item 3 above staff have created a [California Water Boards Racial Equity Data Resource Hub](https://gispublic.waterboards.ca.gov/portal/apps/experiencebuilder/template/?data_id=dataSource_14-0%3A10&id=b02e68db0fa7452aad0b08d3c65d4a93) which will grow as more programs create and publish racial equity based tools and visualizations.

## **Resources**
## Additional Resources

- [Racial Equity at the Water Boards](https://www.waterboards.ca.gov/racial_equity/)
- [Racial Equity Resolution Annotated References](https://www.waterboards.ca.gov/racial_equity/docs/Racial-Equity-Resolution-Annotated-References-2021.11.pdf) (2021)
2 changes: 1 addition & 1 deletion collect-process/process.qmd
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Expand Up @@ -51,7 +51,7 @@ Any aggregation or preliminary analysis of your data should be completed during

You *might* be tempted to begin your data quality assessments as you're looking at the data during this phase, but you'll actually want to hold off on that for now. **You'll complete your [data quality and assurance steps](https://cawaterboarddatacenter.github.io/equity-data-handbook/assure-analyze/qaqc.html)** ***after*** **all of your data have been tidied and you can look at all of your data holistically.**

## Resources
## Additional Resources

- Julia Lowndes and Allison Horst (2020) [Tidy Data for reproducibility, efficiency, and collaboration](https://www.openscapes.org/blog/2020/10/12/tidy-data/). [Openscapes](https://www.openscapes.org/) blog.
- College of Water Informatics [Data Management Handbook](https://www.waterboards.ca.gov/resources/oima/cowi/data_management_handbook.html) - [Collect and Process Section](https://www.waterboards.ca.gov/resources/oima/cowi/data_management_handbook.html#collect_process)
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