Erin Bies | The Zolecki's "New" Training? Same Old, Uninspiring MLM Tactics Repackaged @ErinBies | Uploaded October 2024 | Updated October 2024, 4 hours ago.
The Zolecki's are at it again with their so-called ‘new’ training, but it’s really just the same old, uninspiring tactics they’ve been using for years.
In this video, I break down how they’re trying to repackage outdated methods as something fresh and exciting for their launch of the new Modere products that were tested on 28 of their own Distributors.
A study with a sample size of only 28 people is generally considered insufficient for so many reasons, especially when it comes to making broad generalizations or conclusions about a larger population like Modere is doing with their new product launch. Here are some key points supported by scientific consensus and sources explaining why small sample sizes, like a study of 28 people, may not be effective:
1. Lack of Statistical Power
Small sample sizes reduce the ability of a study to detect true effects or differences (known as statistical power). This increases the likelihood of Type II errors (false negatives), where the study might fail to show an effect even when there is one.
Source:
• Lenth, R. V. (2001). “Some Practical Guidelines for Effective Sample Size Determination.” The American Statistician. JSTOR
2. Limited Generalizability
Studies with small sample sizes often suffer from limited generalizability. Findings from 28 participants may not accurately represent the larger population, especially if there is significant variability in factors such as age, gender, or health status.
Source:
• Ioannidis, J.P.A. (2005). “Why Most Published Research Findings Are False.” PLoS Medicine. doi:10.1371/journal.pmed.0020124
3. Increased Variability and Bias
Small sample sizes are more susceptible to outliers and random variations, which can skew results. A single outlier in a study of 28 participants can disproportionately affect the outcome, making the findings unreliable.
Source:
• Altman, D.G. (1991). “Practical Statistics for Medical Research.” Chapman & Hall.
4. Risk of Overfitting
With fewer participants, small studies risk overfitting the model to the data, meaning that the conclusions may be too tailored to the specific sample and not applicable to other groups.
Source:
• Babyak, M.A. (2004). “What You See May Not Be What You Get: A Brief, Nontechnical Introduction to Overfitting in Regression-Type Models.” Psychosomatic Medicine, 66, 411–421.
5. Publication Bias
Small studies are more likely to be subject to publication bias, where positive or significant results are more likely to be published than negative or inconclusive ones. This skews the overall body of research.
Source:
• Ioannidis, J.P.A. (2008). “Why Most Discovered True Associations Are Inflated.” Epidemiology. 19(5): 640-648.
Don’t be fooled by the hype—it’s still the same MLM playbook. Let’s talk about why this approach isn’t working, and why you should stay away from this kind of training.
#AntiMLM #Modere #MLMExposed #SmallSampleSize #UnreliableResearch
Join this channel to get access to perks:
youtube.com/channel/UCYVGhyc-V7c7bu2pgwqrBeQ/join
FTC Information about MLM: consumer.ftc.gov/articles/multi-level-marketing-businesses-pyramid-schemes
Report Fraud, Medical Claims and Health Claims to the FTC: reportfraud.ftc.gov/#
Contact me at ErinBies.com
Modere Video Mentioned:
youtube.com/live/n_m0uoLgMr8
Instagram Reel Mentioned:
instagram.com/reel/DAWgZiKS_pW/?utm_source=ig_web_copy_link&igsh=MzRlODBiNWFlZA==
** This video was created and published for educational purposes. All opinions are mine and based on my own experiences. Please do not send any hate or negativity to anyone involved in an MLM. The best thing you can do to support them is to listen, be kind and connect them to resources so they can make their own decisions. Not everyone involved with an MLM is a bad person. They are very much a victim of their upline and company that have manipulated them to make money, in my opinion. Everything shared in this video and on this account are protected under the Fair Use Act. **
The Zolecki's are at it again with their so-called ‘new’ training, but it’s really just the same old, uninspiring tactics they’ve been using for years.
In this video, I break down how they’re trying to repackage outdated methods as something fresh and exciting for their launch of the new Modere products that were tested on 28 of their own Distributors.
A study with a sample size of only 28 people is generally considered insufficient for so many reasons, especially when it comes to making broad generalizations or conclusions about a larger population like Modere is doing with their new product launch. Here are some key points supported by scientific consensus and sources explaining why small sample sizes, like a study of 28 people, may not be effective:
1. Lack of Statistical Power
Small sample sizes reduce the ability of a study to detect true effects or differences (known as statistical power). This increases the likelihood of Type II errors (false negatives), where the study might fail to show an effect even when there is one.
Source:
• Lenth, R. V. (2001). “Some Practical Guidelines for Effective Sample Size Determination.” The American Statistician. JSTOR
2. Limited Generalizability
Studies with small sample sizes often suffer from limited generalizability. Findings from 28 participants may not accurately represent the larger population, especially if there is significant variability in factors such as age, gender, or health status.
Source:
• Ioannidis, J.P.A. (2005). “Why Most Published Research Findings Are False.” PLoS Medicine. doi:10.1371/journal.pmed.0020124
3. Increased Variability and Bias
Small sample sizes are more susceptible to outliers and random variations, which can skew results. A single outlier in a study of 28 participants can disproportionately affect the outcome, making the findings unreliable.
Source:
• Altman, D.G. (1991). “Practical Statistics for Medical Research.” Chapman & Hall.
4. Risk of Overfitting
With fewer participants, small studies risk overfitting the model to the data, meaning that the conclusions may be too tailored to the specific sample and not applicable to other groups.
Source:
• Babyak, M.A. (2004). “What You See May Not Be What You Get: A Brief, Nontechnical Introduction to Overfitting in Regression-Type Models.” Psychosomatic Medicine, 66, 411–421.
5. Publication Bias
Small studies are more likely to be subject to publication bias, where positive or significant results are more likely to be published than negative or inconclusive ones. This skews the overall body of research.
Source:
• Ioannidis, J.P.A. (2008). “Why Most Discovered True Associations Are Inflated.” Epidemiology. 19(5): 640-648.
Don’t be fooled by the hype—it’s still the same MLM playbook. Let’s talk about why this approach isn’t working, and why you should stay away from this kind of training.
#AntiMLM #Modere #MLMExposed #SmallSampleSize #UnreliableResearch
Join this channel to get access to perks:
youtube.com/channel/UCYVGhyc-V7c7bu2pgwqrBeQ/join
FTC Information about MLM: consumer.ftc.gov/articles/multi-level-marketing-businesses-pyramid-schemes
Report Fraud, Medical Claims and Health Claims to the FTC: reportfraud.ftc.gov/#
Contact me at ErinBies.com
Modere Video Mentioned:
youtube.com/live/n_m0uoLgMr8
Instagram Reel Mentioned:
instagram.com/reel/DAWgZiKS_pW/?utm_source=ig_web_copy_link&igsh=MzRlODBiNWFlZA==
** This video was created and published for educational purposes. All opinions are mine and based on my own experiences. Please do not send any hate or negativity to anyone involved in an MLM. The best thing you can do to support them is to listen, be kind and connect them to resources so they can make their own decisions. Not everyone involved with an MLM is a bad person. They are very much a victim of their upline and company that have manipulated them to make money, in my opinion. Everything shared in this video and on this account are protected under the Fair Use Act. **
