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  2. Dirty data - Wikipedia

    en.wikipedia.org/wiki/Dirty_data

    Dirty data, also known as rogue data, [1] are inaccurate, incomplete or inconsistent data, especially in a computer system or database. [2]Dirty data can contain such mistakes as spelling or punctuation errors, incorrect data associated with a field, incomplete or outdated data, or even data that has been duplicated in the database.

  3. Data reporting - Wikipedia

    en.wikipedia.org/wiki/Data_reporting

    The effective management of any organization relies on accurate data. Inaccurate data reporting can lead to poor decision-making based on erroneous evidence. Data reporting is different from data analysis which transforms data and information into insights. Data reporting is the previous step that translates raw data into information. [1]

  4. Analytical skill - Wikipedia

    en.wikipedia.org/wiki/Analytical_skill

    It is imperative in inferring information from data and adhering to a conclusion or decision from that data. Data analysis can stem from past or future data. Data analysis is an analytical skill, commonly adopted in business, as it allows organisations to become more efficient, internally and externally, solve complex problems and innovate. [46]

  5. Bias (statistics) - Wikipedia

    en.wikipedia.org/wiki/Bias_(statistics)

    Statistical bias exists in numerous stages of the data collection and analysis process, including: the source of the data, the methods used to collect the data, the estimator chosen, and the methods used to analyze the data. Data analysts can take various measures at each stage of the process to reduce the impact of statistical bias in their ...

  6. Data cleansing - Wikipedia

    en.wikipedia.org/wiki/Data_cleansing

    Data cleansing or data cleaning is the process of identifying and correcting (or removing) corrupt, inaccurate, or irrelevant records from a dataset, table, or database. It involves detecting incomplete, incorrect, or inaccurate parts of the data and then replacing, modifying, or deleting the affected data. [1]

  7. Misuse of statistics - Wikipedia

    en.wikipedia.org/wiki/Misuse_of_statistics

    Outliers, missing data and non-normality can all adversely affect the validity of statistical analysis. It is appropriate to study the data and repair real problems before analysis begins. "[I]n any scatter diagram there will be some points more or less detached from the main part of the cloud: these points should be rejected only for cause."

  8. Bosses are getting Gen Z’s skills deficit all wrong: The ...

    www.aol.com/finance/bosses-getting-gen-z-skills...

    Nearly half (48%) said they want more hard skills training at work, compared to the 33% who said they want more soft skills training, finds Adobe's newly-released survey of more than 1,000 Gen Zers.

  9. Misleading graph - Wikipedia

    en.wikipedia.org/wiki/Misleading_graph

    Though all three graphs share the same data, and hence the actual slope of the (x, y) data is the same, the way that the data is plotted can change the visual appearance of the angle made by the line on the graph. This is because each plot has a different scale on its vertical axis.