Statistics are vital for understanding consumer insights and learning about informational data that could help steer future business decisions. Using statistical tests and their subsequent data can bring awareness to organizational ideas and possibly be used to shape the foundation and structural processes of a business.
Read more at: https://knowledgehound.com/data/what-is-the-difference-between-a-t-test-and-a-z-test/

What Is the Difference Between a T-Test and a Z-Test (made with Spreaker)

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This article, written in a lighthearted tone, bridges the gap between marketers and data analysts. It emphasizes the importance of collaboration between these two departments for successful marketing campaigns.
The article outlines common mistakes marketers make when it comes to data. 

These mistakes include:
1.	Launching campaigns without proper goals or target audience research.
2.	Misinterpreting correlations for causation when analyzing data.
3.	Ignoring data altogether and relying on guesswork.
4.	Getting bogged down in excessive data analysis.
5.	Neglecting the human element and emotional connection in marketing messages.

The article stresses the importance of market research, both through partnering with market research firms and conducting internal research, to understand customer needs and market trends. It also highlights the value of data analytics in guiding marketing efforts.
By working together and leveraging the strengths of both data and creativity, marketers and data analysts can craft targeted and impactful marketing campaigns.


Confessions of a Data Analyst - The Most Common Mistakes Marketers Make (and How to Avoid Them)

This article argues that data can be misleading and provides tips for becoming a more critical consumer of data. It emphasizes that data is only as good as its source and how it's presented. Here are the key takeaways:
•	Be skeptical of data and question its source, methodology, and completeness.
•	Look for data from multiple sources to get a well-rounded view and identify biases.
•	Understand basic statistical terms like averages and medians to better interpret data.
•	Don't take data at face value and fact-check information before drawing conclusions.

Is Your Data Lying To You  3 Red Flags To Watch Out For In Market Research

In this episode of the SuperDataScience Podcast, I chat with Astrophysicist and Online Data Science Instructor, Sam Hinton. You will hear about the Lindau Nobel Laureates meeting, where he met Nobel Prize winners and you will also hear about his appearance on the Survivor TV show. You will learn about quantum mechanics. You will also learn about the course he launched in Python for Statistical Analysis, as well as going in-depth on hypothesis testing. You will hear about Python versus R, statistical significance, why p-value of 0.5 is bad, Bayesian statistics, and what is the difference between frequentist and Bayesian approaches.

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/303

The Super Data Science Podcast: Big Data | Analytics Careers | Mentors | Success

SDS 303: Proper Hypothesis Testing For Every Field

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The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast.

As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact.

Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy.

We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.

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