Statistical Significance
Statistical significance is a crucial concept in research and data analysis, indicating the likelihood that an observed result is not due to chance. It serves as a fundamental tool for determining the reliability of findings and drawing meaningful conclusions from data. By quantifying the probability of obtaining results as extreme as the ones observed, statistical significance aids researchers in discerning patterns and making informed decisions. This statistical measure plays a pivotal role in various fields, including science, economics, and social sciences, guiding the validation of hypotheses and the advancement of knowledge.
Questions
- How high can a p value be?
- If a wholesaler sells to 500 stores and one store shows a 50% uptick in sales, how can the wholesaler determine if this uptick is significant or if it is expected for a few stores to randomly see an uptick of 50%?
- A wholesaler sells goods to hundreds of retailers and knows both the sales of its own products and the total sales of similar goods at each retailer. How can the wholesaler determine if its goods are selling significantly better than competing goods?
- Is it possible to have a p value of 0.00?
- Can a p-value be greater than 1? Why or why not?
- Why is statistical significance not necessarily of practical important difference to a business decision?
- How is p-value related to #alpha#?
- What are some examples when practical significance would outweigh statistical significance?
- What is the power of a statistical test?
- If a relationship has practical significance, does it guarantee that statistical significance will be achieved in every study that examines it?
- How do you choose the level of significance when doing a hypothesis?
- How do I properly interpret a p value?
- How does probability relate to statistical significance?