About the Study in psychology; psychology is an academic and applied discipline that involves the scientific study of human behavior and mental processes.
People who work in this field are called psychologists, psychiatrists, psychotherapists, or counselors, among other titles and jobs.
To practice in any of these areas usually requires a doctorate degree as well as several years of postgraduate training and clinical experience, though some people begin studies with only an undergraduate degree or even no formal preparation at all; doctoral level work can lead to research positions within the academy or to clinical positions involving direct work with patients.
Statistics are used to help psychologists answer research questions, they allow us to make predictions and test hypotheses.
Also, Statistics can be used to describe data, but they can also be used to infer relationships between variables.
In order to accurately interpret statistics, it is important to understand the limitations of the methods used to collect and analyze data, as well as how different statistical tests can lead to different conclusions.
When interpreting results from a statistical analysis, it is important to think critically about whether or not the findings represent a real difference or if they could have occurred by chance alone.
2) Statistical Power
Statistical power is a measure of how likely a study is to detect an effect, if one exists. It’s important to consider when designing and interpreting research, as it can impact the conclusions we draw from our data.
A common way to think about statistical power is by considering the probability that the null hypothesis will be rejected, which depends on three things:
1) The size of the expected effect
2) The sample size
3) The significance level (probability of rejecting the null hypothesis).
A statistical power of 1 means that we are certain that we will be able to detect an effect if it exists, while a statistical power of 0.5 means that there’s a 50% chance you won’t be able to detect an effect, even if one exists, due to random variation.
These numbers often vary depending on what kind of experiment or research design you’re conducting, but they generally fall between 0.8 and 1.0 for experiments involving human subjects.
So what does this mean for your next experiment? One way to increase your statistical power is by increasing your sample size – so try recruiting more participants.
3) Absolute Risk Reduction
Absolute risk reduction (ARR) is a measure of the difference in risk between two groups of people. For example, if the risk of developing cancer is 1% for people who do not smoke and 2% for people who do smoke, then the ARR is 1%.
The ARR is used to compare the risks and benefits of different treatments or lifestyle changes, such as quitting smoking.
An ARR greater than 0 means that the treatment or lifestyle change has reduced the risk of an event more than it increased it.
Conversely, an ARR less than 0 means that it has increased the risk more than it reduced it, while an ARR equal to 0 means that there was no effect on the risk. Note: Risk ratio is similar but may be easier to interpret in some cases.
When someone’s risk increases by 10%, their relative risk increase would be 10%. But when someone’s risk decreases by 10%, their relative risk decrease would also be 10%.
So, the larger the absolute value of the difference in those two percentages, the greater the impact, whether it’s negative or positive.
You can think of this in terms of probability too: A 50% chance goes up to 100%; while a 50% chance goes down to 25%.
The difference between two probabilities would be 10%.
If one number was 90% and another 80%, then their difference is 0.8. If both numbers were 45%, their difference is 0.9.
The second column below shows what happens when we subtract a number from its opposite, a little bit confusing at first, but it makes sense once you get your head around it.
It turns out that a negative percentage represents something going backwards.
So with our previous example, a 10% increase is equivalent to 10-10=0.1×100=10%. Likewise, a 10% decrease is equivalent to 100-10=90×100=90%. To summarise
4) Relative Risk Reduction
In medical research, relative risk reduction (RRR) is the ratio of the absolute risk reduction to the baseline risk. For example, if the baseline risk of developing a disease is 10% and treatment A reduces the risk by 3%, then the RRR for treatment A is 30% (3% ÷ 10%).
The concept of RRR is used to communicate to patients and clinicians alike the potential magnitude of benefit that can be expected from a therapy, as well as how it compares with other therapies.
An RRR of 50% may not sound like much, but when you compare it to an average baseline risk of 10%, it’s equivalent to reducing the risk by 5%.
On the other hand, an RRR of 90% might sound more impressive than 50%.
But when you compare it to an average baseline risk of 20%, 90% is only equivalent to reducing the chance of getting a disease from 20% down to 12%.
The RRR tells you only how a therapy stacks up against a common background risk.
However, it doesn’t address important considerations such as whether your chance of benefiting from treatment is likely to be higher or lower.
If, for instance, one group has a high probability of developing the condition in question and another group has low probability despite having the same absolute risk then the RR will reflect this difference.
So you should use RR to assess which treatments are better than others, but also consider other factors such as cost-effectiveness.
In conclusion, it is important to remember that these studies are only focusing on one part of our brain.
There are other regions of the brain that could be responsible for this effect.
It would be interesting to find out if there are any implications with memory retention and attention span.
Lastly, it is not just a matter of taking care of our brains but also using them so we don’t lose them!