¿Cómo hacer un EXPERIMENTO? | Las relaciones causales en marketing y tutorial paso a paso

Understanding Experimental Techniques in Data Collection

Introduction to Experiments

• The video introduces quantitative data collection techniques, focusing on experiments as a fun method for assessing variable changes under different conditions.

• Experiments allow researchers to measure causal relationships, determining if one variable (X) impacts another (Y).

Causal Relationships

• In the context of smoking, X represents smoking behavior while Y reflects health effects, specifically lung health.

• A price reduction in a store can serve as another example where X (the discount) may lead to various outcomes such as increased sales or negative perceptions.

Designing Effective Experiments

• Proper experimental design is crucial for isolating effects and ensuring accurate measurement of variables without interference from other factors.

• Three key requirements for establishing causality include:

• Correlation between variables must exist.

• The cause must precede the effect.

• The cause should be the sole explanation for the observed effect.

Isolating Variables

• To accurately assess causation, it’s essential to isolate both the cause and effect within an experiment to avoid confounding influences from other variables.

• Researchers create distinct groups: a control group that remains unchanged and an experimental group where specific conditions are applied.

Practical Application Example

• An example involves studying lung health among patients who have quit smoking versus those who relapse.

Understanding Experimental Design in Marketing

The Importance of Control Groups

• The discussion begins with an example of marketing strategies, specifically focusing on sales promotions in two stores without initial discounts.

• After implementing a discount in one store, the speaker emphasizes the need to compare sales data from both stores to determine if the discount influenced customer behavior.

• It is highlighted that external factors (e.g., seasonal changes) could affect sales, underscoring the necessity of having a control group for accurate analysis.

Variables in Experiments

• The speaker introduces the concept of dependent and independent variables using examples from experiments, stressing their significance in understanding outcomes.

• The dependent variable is defined as what researchers aim to measure or understand—in this case, lung health and sales perception by consumers.

• Independent variables are those that influence the dependent variable; they include contextual factors like product variety and promotional frequency.

Examples Illustrating Variable Relationships

• Several examples clarify how independent variables affect dependent ones:

• Fertilizers and Plant Growth: Fertilizer usage impacts plant size; thus, fertilizer is independent while plant size is dependent.

• Product Purchase Influenced by Packaging Color: Here, color affects purchasing decisions—color being independent and purchase being dependent.

• Another example discusses how mood influences product evaluation; mood acts as an independent variable affecting consumer ratings (dependent).

Weather's Impact on Sales

• A final example illustrates how weather conditions can influence coat sales—rainy weather increases demand for coats, making weather an independent variable while coat sales are dependent.

Monitoring Uncontrollable Variables

• The importance of recognizing uncontrollable variables such as age or time of year during experiments is discussed. These may impact results even if not directly manipulated.

• Researchers should monitor these additional variables to ensure comprehensive analysis and account for any unforeseen effects on experimental outcomes.

Statistical Validity in Experimental Results

Importance of Statistical Testing

• Demonstrating the validity of results requires more than just showing differences between two groups; statistical tests are necessary to confirm these findings.

• For measuring outcomes before and after a condition within the same group, paired sample means should be used since it involves the same individuals.

Types of Statistical Tests

• To assess changes in a single group's performance (e.g., smoking effects), a paired sample mean test is appropriate due to the repeated measures on the same subjects.

• When comparing sales data from two different stores before and after a discount, independent samples must be analyzed as they involve distinct groups.

Application of Independent Samples Test

• An independent samples test is essential for determining if one group has significantly increased sales compared to another, highlighting differences in average sales performance.

Understanding Experimental Characteristics