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You're facing sample selection bias in your market research. How can you ensure the validity of your results?

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In the realm of marketing research, ensuring the validity of your results is paramount. Sample selection bias, a sneaky and often unnoticed error, can skew your findings and lead to misguided business decisions. This bias occurs when the sample, or group of individuals chosen for the study, is not representative of the target population. The consequences? You might end up with insights that don't accurately reflect the preferences, behaviors, or opinions of your intended audience. But fear not, there are strategies you can implement to minimize this risk and bolster the credibility of your research.

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Paul Masao

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TRAORE Abdoul-Kader

Géologue d’exploration / Environnementaliste / Secrétaire à l’organisation et aux relations extérieures de l’Alliance…

1 Define Bias

Sample selection bias can significantly affect the outcome of your market research. It happens when certain members of your intended population are more likely to be included in your sample than others, leading to results that are not truly representative. To combat this, start by understanding the types of bias that can occur, such as self-selection bias, where individuals volunteer for a study, possibly because they have strong opinions that don't match the broader population. Recognizing these biases is the first step towards ensuring the validity of your research findings.

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Paul Masao

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Bias in sampling can cause a very big problem in resource modeling leading into improper investment in mining and eventually loss of capital.

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2 Diverse Sampling

To mitigate sample selection bias, diversifying your sampling methods is key. Rather than relying on a single source, use a mix of channels to reach potential participants. This could include online platforms, direct outreach, and partnerships with organizations that have access to different segments of your target market. The goal is to create a sample that mirrors the diverse characteristics of your entire population, thereby reducing the likelihood of over-representing or under-representing certain groups.

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Paul Masao

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In geology and mining diverse sampling enables the closer and clear understanding of the deposit eventually allowing proper investment.

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TRAORE Abdoul-Kader

Géologue d’exploration / Environnementaliste / Secrétaire à l’organisation et aux relations extérieures de l’Alliance pour la Gouvernance de l’Eau et pour le Développement AGED
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Il est très important de diversifier l’échantillonnage en géologie car cela vous permet d’avoir plus d’information ce qui permet de faire des bonnes analyses et interprétations afin d’obtenir des résultats pertinents.

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3 Randomize Selection

Random selection is your best defense against sample selection bias. This means every individual in your target population should have an equal chance of being chosen for your study. Use random number generators or other unbiased selection methods to pick your participants. This approach helps ensure that your sample is not swayed by any particular characteristics, making your results more reliable and reflective of the broader population.

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Paul Masao

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Randomizing sampling allow to know whether there was bias sampling in geology. However, quality control and quality sampling measures are the best controls in sampling to measure biased sampling.

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4 Stratify Your Sample

Stratification is a technique where you divide your target population into subgroups based on key characteristics and then randomly select samples from each subgroup. By doing so, you ensure that important segments of your population are adequately represented in your study. For instance, if age is a relevant factor for your research, you would stratify your population into different age groups and then randomly select from within those groups.

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Paul Masao

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Quality control and quality assurance stratify your sampling whereby in every group of sampling a known sample with known value is inserted and analyzed together with your samples. If in such group of lets say 20 samples the value of the known sample is not achieved then the group in reanalyzed.

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5 Monitor and Adjust

Constantly monitor your sampling process for signs of bias. If you notice certain groups are over- or under-represented, take corrective action immediately. This might involve adjusting your outreach methods or revisiting your sampling criteria. It's also important to keep track of who declines to participate and consider the reasons why. This information can offer valuable insights into potential biases and help you refine your approach.

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Paul Masao

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All sample analysis are frequently and regularly monitored before included into the model. This will eliminate biases in sampling and provide accurate end results.

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TRAORE Abdoul-Kader

Géologue d’exploration / Environnementaliste / Secrétaire à l’organisation et aux relations extérieures de l’Alliance pour la Gouvernance de l’Eau et pour le Développement AGED
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Respecter les procédures d’échantillonnage en géologie sont cruciales. Un échantillonnage mal fait entra?nera une contamination des échantillons ce qui produira de résultats biaisés.

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6 Validate Findings

Finally, validating your findings is crucial for confirming the absence of sample selection bias. Cross-check your results with external data sources or conduct follow-up studies using different samples. If the results consistently align, you can be more confident in the validity of your research. Remember, validation is not a one-time task but an ongoing process that strengthens the credibility of your findings over time.

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TRAORE Abdoul-Kader

Géologue d’exploration / Environnementaliste / Secrétaire à l’organisation et aux relations extérieures de l’Alliance pour la Gouvernance de l’Eau et pour le Développement AGED
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Les QA/QC sont une aide de décision pour validé les résultats. Les résultats des contr?les obtenus du laboratoire doivent être comparés aux résultats réels connu. La marge d’erreur doit être se 0.10. Si cette marge est respectée les résultats peuvent être validé.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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You're facing sample selection bias in your market research. How can you ensure the validity of your results?

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1 Define Bias

2 Diverse Sampling

3 Randomize Selection

4 Stratify Your Sample

5 Monitor and Adjust

6 Validate Findings

7 Here’s what else to consider

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