• Describe how the idea of causal inference can apply to your interactions in everyday life. Also, make an argument for how the idea of causal inference could be applied the way we approach ministry.
• Begin by explaining what the p value reveals about the probability that a study is replicable. Next. describe the major alternatives to the use of α < .5 (NHST). What is p-hacking? Describe common researcher practices can be described as p-hacking and how research results might be implicated as a result of p-hacking. Moreover, how are future research studies which are attempting to replicate these studies implicated as a result of p-hacking?
We can apply causal inference to ministry to more effectively understand the impact of our actions and programs. Instead of simply assuming a program is working, a causal inference approach would ask, "Did this specific intervention cause the desired outcome?" For example, if a ministry starts a new youth group program and sees an increase in attendance, a causal inference approach would seek to determine if the program was the cause of the increase. This could involve comparing the attendance of the new group to a similar group that didn't receive the new program or by looking at attendance trends before and after the program's implementation. By using a causal inference lens, ministry leaders can move beyond simple correlation and better understand what truly works, allowing them to allocate resources more effectively and create more impactful initiatives.
P-values, Statistical Significance, and P-hacking
The p-value does not reveal the probability that a study is replicable. It represents the probability of observing the obtained data (or more extreme data) if the null hypothesis were true. A low p-value (typically less than 0.05) suggests that the observed result is unlikely to have occurred by random chance, leading to the rejection of the null hypothesis. However, the p-value says nothing about the likelihood of the result being true or reproducible. Replicability is about whether the same results can be obtained under the same conditions in a new, independent study. A significant p-value in one study does not guarantee a significant p-value in a future one.
Alternatives to NHST
Major alternatives to the conventional α < 0.05 (NHST) approach include:
Bayesian Statistics: This approach uses probability to express a researcher’s belief in a hypothesis before and after collecting data. It provides a measure of evidence for a hypothesis rather than just a p-value for a null hypothesis.
Effect Size Reporting: Reporting the magnitude of an effect (e.g., Cohen's d, odds ratios) provides a more complete picture of the results than a p-value alone. A statistically significant result might have a very small effect size, which is not practically meaningful.
Confidence Intervals: A confidence interval gives a range of plausible values for a population parameter, such as the mean difference between two groups. A 95% confidence interval for a difference score that does not include zero suggests a statistically significant result, while also providing information on the precision of the estimate.
P-hacking
P-hacking is the practice of manipulating data or statistical analyses to produce a statistically significant p-value, even when the underlying effect is not truly significant. Researchers might do this to increase the chances of getting their study published, as journals traditionally favored studies with significant results.
Common researcher practices that constitute p-hacking include:
Optional Stopping: Collecting more data until the p-value becomes significant, then stopping the study.
Sample Answer
Causal Inference in Everyday Life and Ministry
Causal inference is the process of determining a cause-and-effect relationship. In everyday life, we use it constantly to make sense of the world. For example, if you notice your car won't start after you left the headlights on, you infer that leaving the lights on caused the battery to die. You are making a causal connection. Another example is if you get a headache and then take a specific painkiller, and the headache goes away. You infer that the painkiller caused the headache to subside. We apply this principle to make predictions and decisions, such as remembering to turn off the lights next time or to use the same painkiller again.