10.4 Interpreting Results and Drawing Conclusions

Statistical analysis is crucial in marketing research. It helps determine if findings are meaningful or just chance occurrences. Researchers use p-values, confidence intervals, and effect sizes to interpret results, considering both statistical and practical significance.

Communicating results effectively is just as important as the analysis itself. Researchers use tables, graphs, and written reports to present findings clearly. They must consider their audience, highlight key insights, and acknowledge limitations to ensure proper understanding and application of the results.

Interpreting and Communicating Results

Interpretation of statistical output

Top images from around the web for Interpretation of statistical output

• 

  Why It Matters: Summarizing Data Graphically and Numerically | Concepts in Statistics View original

  Is this image relevant?

• 

  A Closer Look at Tests of Significance | Boundless Statistics View original

  Is this image relevant?

• 

  Frontiers | P-Value, Confidence Intervals, and Statistical Inference: A New Dataset of ... View original

  Is this image relevant?

• 

  Why It Matters: Summarizing Data Graphically and Numerically | Concepts in Statistics View original

  Is this image relevant?

• 

  A Closer Look at Tests of Significance | Boundless Statistics View original

  Is this image relevant?

1 of 3

Top images from around the web for Interpretation of statistical output

• 

  Why It Matters: Summarizing Data Graphically and Numerically | Concepts in Statistics View original

  Is this image relevant?

• 

  A Closer Look at Tests of Significance | Boundless Statistics View original

  Is this image relevant?

• 

  Frontiers | P-Value, Confidence Intervals, and Statistical Inference: A New Dataset of ... View original

  Is this image relevant?

• 

  Why It Matters: Summarizing Data Graphically and Numerically | Concepts in Statistics View original

  Is this image relevant?

• 

  A Closer Look at Tests of Significance | Boundless Statistics View original

  Is this image relevant?

1 of 3

• Statistical significance assesses the likelihood of observing the results if the null hypothesis is true
  • p-value represents the probability of obtaining the observed results assuming the null hypothesis is correct
    • p < 0.05 is widely accepted as the threshold for statistical significance (α level of 0.05)
  • Confidence intervals provide a range of plausible values for the true population parameter
    • Narrower intervals suggest more precise estimates (95% CI, 99% CI)
• Effect size quantifies the magnitude or strength of the difference or relationship between variables
  • Various measures include Cohen's d (standardized mean difference), correlation coefficients (Pearson's r, Spearman's ρ), odds ratios (OR)
  • Interpret effect sizes within the context of the research question and domain (small, medium, large effects)

Significance of results

• Practical significance focuses on the real-world implications and meaningfulness of the findings
  • Consider the size of the effect or difference in a practical sense
  • Evaluate the impact of the results on decision-making, policy implementation, or clinical practice (minimum clinically important difference)
• Statistical significance does not necessarily imply practical significance and vice versa
  • Large sample sizes can detect statistically significant results even with small effect sizes (overpowered study)
  • Results with large effect sizes may not reach statistical significance in small samples (underpowered study)

Limitations and error sources

• Sampling limitations can affect the representativeness and generalizability of the results
  • Non-representative samples may not accurately reflect the target population (convenience sampling)
  • Small sample sizes reduce statistical power and precision (pilot studies)
  • Sampling bias can occur due to non-random selection or participation (self-selection bias)
• Measurement limitations impact the accuracy and consistency of the data collected
  • Validity refers to the extent to which a measure assesses the intended construct (face validity, construct validity)
  • Reliability indicates the consistency of measurements across time, raters, or items (test-retest reliability, inter-rater reliability, internal consistency)
  • Response biases can influence participant responses (social desirability bias, acquiescence bias)
• Design limitations may introduce confounding factors or limit the strength of causal inferences
  • Lack of control for potential confounding variables can lead to spurious associations (age, gender, socioeconomic status)
  • Insufficient randomization or blinding can introduce bias (selection bias, performance bias, detection bias)
• Generalizability refers to the extent to which the results can be applied to other contexts
  • Results may be specific to the studied population, setting, or timeframe (college students, laboratory settings, short-term outcomes)

Communicating Findings

Communication of findings

• Tables present data in a structured and organized format
  • Include relevant descriptive statistics, p-values, and confidence intervals
  • Use clear row and column headers, decimal alignment, and appropriate number of significant figures
  • Provide informative titles and footnotes to enhance understanding
• Graphs visually represent patterns, trends, and relationships in the data
  • Choose graph types based on the nature of the data and research question (bar charts for categorical comparisons, line graphs for trends over time, scatterplots for correlations)
  • Include meaningful titles, axis labels with units, and legends to facilitate interpretation
  • Maintain appropriate scales and avoid distorting the data (truncated y-axis, logarithmic scales)
• Written reports communicate the key aspects of the study in a clear and concise manner
  • Summarize the research question, study design, methods, and main findings
  • Interpret the results in light of the research objectives and existing literature (convergent or divergent findings)
  • Discuss the implications of the findings, study limitations, and future research directions
  • Tailor the language and level of detail to the target audience (scientific community, policymakers, general public)