VR Research Cave

With lab mates from Wallace Lab helping set up the VR immersive environment cave being installed at my research lab.

I'm going to get to use this cool tech in my research design to study sensorimotor issues in autism. 

p-value

p-value for the layman

Statistics can often feel like a labyrinth of complex numbers and jargon. In the world of statistics, p-values are your compass. While the concept may seem a bit abstract at first, p-values are like a traffic light for your scientific discoveries, guiding you to proceed with caution or giving you the green light to embrace a new understanding of the world.

What is a p-value?

At its core, a p-value is a number that helps us determine the significance of an observation or result in statistical analysis. Imagine you've conducted an experiment or a survey, and you want to know if your findings are meaningful or just a result of chance. The p-value is your guide.

The Role of Probability

To grasp p-values, you need to understand the concept of probability. Think of it as a measure of how likely something is to happen. In statistics, we often want to know the probability of observing certain data if there's no real effect or difference. This is where p-values come into play.

Hypotheses: The Foundation

In any scientific study, you start with two hypotheses: the null hypothesis (H0) and the alternative hypothesis (H1). The null hypothesis represents the idea that there's no significant effect or difference, while the alternative hypothesis suggests the opposite – that there is a significant effect or difference.


The Experiment and the Data

You gather your data, conduct your analysis, and calculate a test statistic, which quantifies the difference between your observed data and what you would expect under the null hypothesis. This test statistic follows a particular distribution, like the normal distribution for many common statistical tests.

The P-Value's Revelation

Here's the moment of truth: the p-value tells you the probability of obtaining a test statistic as extreme as, or more extreme than, the one you calculated if the null hypothesis is true. In simpler terms, it answers the question: "How likely is it that my observed results are just due to random chance?"

Interpreting P-Values

Now, the key interpretation comes into play. If your p-value is small, typically less than 0.05 (but it can vary depending on the field), it suggests that your observed results are unlikely to have occurred by chance alone. This is your green light to reject the null hypothesis and accept that you've found something significant.

Conversely, if your p-value is large (greater than 0.05), it indicates that your observed results are quite likely to be explained by random chance, and you should stick with the null hypothesis.

It's Not Absolute Proof

One crucial thing to understand is that p-values don't provide absolute proof or disproof. They offer a level of evidence, but they can't tell you the size of an effect or whether it's practically meaningful. They merely guide you in determining if your results are statistically significant.

Null but Noteworthy Results

[Concepts in Research] 

Unearthing Hidden Gems: The Power of Null but Noteworthy Results. 

We live in a world obsessed with "success" and "results," where everyone craves those flashy headlines and dazzling breakthroughs. But there is also that captivating realm of science where the unsung heroes of research reside: the "Null but Noteworthy" results!

Picture this: scientists, huddled in labs, fervently running experiments, only to be met with a lack of statistically significant findings. It's when scientists don't get that big "Eureka!" moment they hoped for, meaning their experiments didn't yield any jaw-dropping, statistically significant results. 

In the world of research, null results are sometimes brushed aside like yesterday's news. But here's the kicker – they still have a story to tell.  Imagine you're digging for treasure, and instead of finding gold, you stumble upon ancient artifacts that offer a glimpse into an unknown civilization. Those artifacts might not be shiny, but they are undoubtedly noteworthy!

Our pursuit of knowledge should never be solely about finding "yes" or "no" answers. Embracing "Null but Noteworthy" results sparks curiosity, opening doors to entirely new avenues of inquiry. Like a maze with countless paths, these unassuming results may hold the key to groundbreaking revelations.

By acknowledging and sharing these seemingly modest findings, researchers foster an environment of honesty and integrity in science. No more sweeping those "unsuccessful" studies under the rug! It's time to celebrate the courage it takes to publish these results and the potential they have to refine our understanding of the world.

I think we need to remember that in a universe brimming with complexity, not every puzzle piece fits perfectly – and that's okay! These "Null but Noteworthy" results serve as guideposts for future investigations, leading us towards answers that might have otherwise remained hidden.

So, the next time you stumble upon a study with lackluster headlines, pause for a moment and give it a chance. Embrace the power of "Null but Noteworthy" - you never know what intriguing revelations might lie beneath the surface.

Stay curious, stay bold, and let's celebrate the beauty of scientific exploration in all its forms! 🧠

Attention Check Questions

In my grad school journey or learning to do research, I come across many interesting concepts. Here's one.

Attention check questions, sometimes called validity checks or instructional manipulation checks, are typically included in a survey or questionnaire to ensure that respondents are reading and fully understanding the questions. They serve as a way to assess whether participants are paying attention and not just rushing through or randomly answering questions in order to collect payment. They help improve the reliability and validity of the data collected in a survey.

An example of a simple attention check question could be "Please select 'Somewhat agree' for this question." If a respondent doesn't select 'Somewhat agree,' it can be inferred that they aren't reading the questions carefully, which could invalidate their other responses.

More complex attention check questions might be embedded within the content of the questionnaire. For instance, you might ask a question where the correct answer is obvious or already stated in the questionnaire, or where the answer should be logically consistent with previous responses.

Such checks are important when you're conducting research that relies on self-reported data, as they can help you filter out unreliable responses. However, they should be used judiciously. If used excessively or inappropriately, they can frustrate participants or create bias in your results. They should not be designed to trick respondents or make them feel foolish, and respondents should be informed at the start of the survey that their responses will be checked for consistency and attentiveness

There's no hard and fast rule about where attention check questions should be placed in a questionnaire, as it often depends on the specifics of the questionnaire and the goals of the researcher. 

Some general guidelines

• Spacing: For a lengthy survey, it may be good to sprinkle several attention checks throughout the survey. They shouldn't be too close together, as that might be annoying or confusing for the respondents. The goal is to check for consistent attention throughout the survey, so they might be placed at regular intervals. For example, if you have a 50-question survey, you could place an attention check question after every 10 or 15 questions.
• Variety: of attention check question types means participants can't easily identify them and respond correctly without paying attention to the rest of the survey.
• Placement in Context: The questions can sometimes be related to the subject matter of the survey. In this case, they should be placed where they make the most sense in the context of the other questions.
• Randomization: If possible, randomizing the order of questions, including attention checks, can help avoid bias that might result from their position in the survey.
• Placement in Important Sections: If there are certain sections of the survey where it is particularly important that respondents are paying attention (e.g., complex questions or key measures), it might make sense to include an attention check question immediately before or after that section.
• Avoiding End or Start: At the start, respondents are usually more attentive, and at the end, they may be rushing to finish. Hence, these locations may not accurately capture the participant's overall level of attention.