Using statistics in management and business research is not a "weak force"; in fact, it is a powerful and essential tool. While it's true that statistical methods can be complex and must be used carefully to avoid errors, they provide powerful insights that can significantly improve business outcomes. Statistics allows researchers and managers to make decisions based on data rather than intuition or anecdotal evidence. In business and management, these decisions often involve risk and uncertainty, and statistics helps quantify this risk. For example, through statistical analysis (such as regression analysis), companies can predict customer behavior, forecast sales, or evaluate the effectiveness of a new marketing strategy. Proper use of statistical techniques can enhance the reliability and validity of research findings. One of the core strengths of statistics is its ability to generalize findings from a sample to a broader population.

It is supportive tool. Qualitative and phenomenological research is best. Quantitate research is low grade research mostly used in empirical and experiments based research where cold date on variables are recorded and tested on various statistical tools to make out sense.

I don't quite get what you mean by "a weak force for research". As for me, being in the business of marketing and business research, the use of statistics in management and business research is a powerful tool rather than a weak force. Statistical methods enable researchers to analyze data, identify patterns, and make evidence-based decisions, which are critical in understanding market trends, customer behaviors, and operational efficiencies. Speaking from experience, here’s how statistics are used in the commercial world:

1. Data-Driven Insights

• Example: A retail company analyzes transaction data to understand purchasing patterns. By applying clustering techniques, they discover distinct customer segments, such as price-sensitive shoppers and luxury-oriented buyers. This allows the company to tailor marketing campaigns to each segment, significantly increasing sales conversion rates.

2. Risk Management

• Example: In insurance, statistical models like actuarial analysis are used to estimate risk and set premiums. For instance, auto insurance companies use regression models to predict accident likelihood based on factors like driver age, location, and vehicle type.

3. Decision-Making

• Example: A marketing team compares the performance of two advertising strategies using A/B testing. By analyzing customer responses, they determine the more effective campaign and allocate their budget accordingly.

4. Quality Control and Improvement

• Example: Six Sigma methodologies, rooted in statistical analysis, are implemented by a beverage company to minimize product variability, leading to higher customer satisfaction and reduced waste.

5. Market and Consumer Research

• Example: A fashion brand uses conjoint analysis, a statistical method, to determine which product features (e.g., fabric type, price, color) are most valued by customers, helping them prioritize features in new product designs.

These examples show that when applied correctly, statistics empower businesses to make informed, data-driven decisions, mitigate risks, improve operations, and tailor strategies to consumer needs. It's far from being a weak force.

Thank you. I see a lot of business management researchers talking about increasing the statistical ratio and reducing the theoretical and debate ratio. And that's why I asked the question.