The parameter of interest is the one statistic about a random event that the researcher wants to know more about.
For example, if you’re studying what time of day people are most likely to go on Facebook, then “time” would be the parameter of interest. For another study, it might be geographic location or number of friends–meaning if I knew those values for each individual and was interested in looking at how they related to some other variable (like height) I could determine whether there’s a correlation between these two variables.
Parameter of interest is a descriptive statistic, often labeled as α=?
When researchers are interested in whether two groups differ from one another, the parameter being of interest is usually the null hypothesis H0- that they are not different. For example, when considering whether the voting habits of conservative versus liberal people are different then we might describe α= 0.05- meaning that we have a 5% chance of incorrectly concluding that there is a difference where none exists.
If an experiment doesn’t generate enough data to reject or support H0 (often because it wasn’t large enough), at this point our parameter b would be our unknown value for β – the population parameter and we should not make formal inferences about the population.
The parameter of interest (α) is the probability that we would make an incorrect decision about H0. The larger α needs to be, the more stringent our tests must become, because it becomes less likely that we will reject H0 when it is true.
For example, if α= 0.01 then only 1% of the time H0 could be rejected when it is true.
A significance level, or p-value, measures how extreme a test statistic is in relationship to the null hypothesis. The null hypothesis represents no difference between groups so the comparison needs to be made against this value. If you are comparing two different treatments then your parameter of interest is no longer the null hypothesis but rather the difference between treatments. In this case you would reject H0 if the difference between treatments was sufficiently large.
The term parameter of interest can be used in many different ways depending on who is using it and for what purpose. The most common use relates to statistical testing where it represents a test statistic of interest.
Parameter of interest in statistics means any parameter that you’re analyzing or studying.
Information to include: Exemplifying answer with a thesis statement and conclusion
A thesis statement: Parameters of interest are studied in an effort to determine which variables have a statistically significant correlation with the dependent variable.
A conclusion: Parameters of interest often provide helpful insight into developments we would not otherwise know much about. For example, changes in body temperature can act as a time-lapse indicator for many physiological processes; from the digestion process to (possibly) even cancerous growths.
Thesis Statement: Variables that affect duration and frequency are parameters of interest when looking at predictive processes between independent variables and the response variable.
This is a difficult question.
The word parameter has been used in statistics (and other sciences) since the 1800s as an alternative to more common, but not more precise, words like “variables” or “quantities”. Confusion can arise because parameters are quantities of interest—so some statisticians have proposed that parameter should mean only something that could be identified without knowing the model’s form. If this were true, it would preclude parameters describing both the distribution density and its mean or variance. The parameter of interest typically depends on what questions are being asked. For example, we could be interested in using a model whose form is unknown to estimate population means for parameters such as mixtures between two known distributions with unknown densities. This would mean using the model to estimate two quantities of interest—the mixture proportions and the mixture densities. The parameters of interest here are the proportions, not the densities.
A parameter of interest in statistics is simply a parameter that the statistician needs to know about in order to run the statistical analysis. So a parameter of interest might be age, or height, or weight.
Parameter of interest is a statistic that helps determine the degree to which another statistic (such as sampling error) measures variation in the population.
It means just that.
The statistician’s parameter of interest is whatever they were seeking and estimating, the central situation, on which they want to focus their analysis.
An example might be a study investigating the correlation between abdominal circumference and lung function, with their central data point being abdominal circumference as an explanatory variable for lung function. They would then examine how varying other parameters (e.g., pressure) impacted this correlation–not because they care about those other variates so much (although that could also happen), but because in order to make any generalizations complete enough to be helpful or informative, you need to determine what the effects are on your parameter of interest when you change something else within your systems.
It is the variable that statisticians are looking for a relationship between. It is usually chosen based on some research or observation and then other variables (not the parameter of interest) will be tested to see if they have an effect on this parameter. For example, suppose statistician wants to know what effects distance has on speed in people’s daily commute to work. One would start by saying “distance” is the parameter of interest and so they should collect this data from their study participants by recording how far away their home vs. workplace was and how fast they travelled each way. The experiments, in turn, could involve altering something other than distance (e.g., increasing the percentage of time that the person takes public transit instead) and testing if this had any effect on speed.
A parameter of interest is a variable that a statistician wants to know more about.
A statistician will look for parameters of interests and go about designing collection methods that will allow them to measure these parameters more accurately than other parameters. To drive this home, there’s an example from my dissertation. The parameter of interest was average household income by the number of licensed drivers in the homeowners’ households. I went with this because sometimes adding something like “drivers licenses” at the end of your address means you’re living in a large house with many people (increasing probability for greater wealth). So I needed to develop a data collection method better than just getting zipcode, not knowing whether they live alone or with their family, as well as other interesting stuff like that. I went with a mailback survey after receiving census data and determining that many of the zipcodes had high income and lots of households (meaning I would not need to do direct observation). Since then, we’ve improved this method by using geo-coding from latitude/longitude so now we have income for those who’ve moved away. I don’t think any of the other students had to deal with determining collection methods like this, but hey, why do something easy if you can do it hard?
I hope that helped!
A parameter of interest is anything that can be calculated about a data set or population, for the sake of analysis.
The parameters most often used by statisticians are the mean, variance, and standard deviation. The three parameters define the shape of a particular probability distribution. For example, in general, one would expect points sampled from a uniform distribution to have an equal number with each value between -∞ and ∞. This means we should see some values less than 0, more than 0 but not too many more than 0 (so both positive numbers and negative numbers), then some values greater than 0 but not too many less than ∞ (meaning we want to make sure we have numbers other than zero) etc.