Update analysis.qmd

assure-analyze/analysis.qmd

@@ -10,7 +10,7 @@ Ideally, the data-informed decisions we make and the corresponding actions we ta
 
 ![Illustrations of two perspectives on Data-Information-Knowledge-Wisdom (DIKW) In practice. Graphic adapted from Flood et al. (2016) and DataCamp (2023).](images/dikw_adapted.png)
 
-As the graphic above illustrates, the transition and transformation from data to wisdom requires adding context, meaning, insight to the original data, while gaining experience and understanding as we progress. In the above graphic we define data, information, knowledge, and wisdom as:
+As the graphic above illustrates, the transition and transformation from data to wisdom requires adding context, meaning, and insight to the original data, while gaining experience and understanding as we progress. In the above graphic we define data, information, knowledge, and wisdom as:
 
 -   **Data**: individual measurements, facts, figures, and signals, without context.
 
@@ -24,9 +24,9 @@ As the graphic above illustrates, the transition and transformation from data to
 
 Turning data into information in the context of racial equity involves navigating complex ethical considerations. The process requires an understanding of the potential impact on Black, Indigenous, and other People of Color (BIPOC) communities and the responsibility to mitigate perpetuating or reinforcing biases. Upholding ethical standards requires a commitment to maintaining privacy, accessibility, and fostering transparency throughout the data transformation process. Additionally, acknowledging the limitations of the data and being transparent about potential biases is essential for maintaining the integrity of the data and information generated and shared. The transformation of racial equity data into meaningful information requires a thoughtful and intentional approach which we will highlight in the next sections.
 
-For example, many programs will rely on demographic and socioeconomic data, like those collected from the [U.S. Census](https://www.census.gov/programs-surveys/decennial-census/decade/2020/2020-census-main.html) and the [American Community Survey](https://www.census.gov/programs-surveys/acs) (ACS). Because the ACS is based on a sample, rather than all housing units and people, ACS estimates have a degree of uncertainty associated with them, called sampling error. In general, the larger the sample, the smaller the level of sampling error. To help users understand the impact of sampling error on data reliability, the Census Bureau provides a “margin of error” for each published ACS estimate. The margin of error, combined with the ACS estimate, gives users a range of values within which the actual “real-world” value is likely to fall. Also see: [Using American Community Survey (ACS) Data: What All Data Users Need to Know Handbook](https://www.census.gov/programs-surveys/acs/library/handbooks/general.html).
+For example, many programs will rely on demographic and socioeconomic data, like those collected from the [U.S. Census](https://www.census.gov/programs-surveys/decennial-census/decade/2020/2020-census-main.html) and the [American Community Survey](https://www.census.gov/programs-surveys/acs) (ACS). Because the ACS is based on a sample, rather than all housing units and people, ACS estimates have a degree of uncertainty associated with them, called sampling error. In general, the larger the sample, the smaller the level of sampling error. To help users understand the impact of sampling error on data reliability, the Census Bureau provides a “margin of error” for each published ACS estimate. The margin of error, combined with the ACS estimate, gives users a range of values within which the actual “real-world” value is likely to fall. Also see: [Using American Community Survey (ACS) Data: What All Data Users Need to Know Handbook](https://www.census.gov/programs-surveys/acs/library/handbooks/general.html).
 
-It is important to acknowledge this uncertainty up front to be transparent with your audience about the data and conclusions you are drawing. 
+It is important to acknowledge this uncertainty up front to be transparent with your audience about the data and conclusions you are drawing.
 
 Examples of how others have done this work include:
 
@@ -121,7 +121,7 @@ As with any analysis, we need to understand the limits of our data and the metho
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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.** |
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-| ![](images/data-fallacy_regression-toward-mean.png) |                                                                                                                                                 | If our collective history and it's 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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