A researcher analyzes data and finds a statistically significant relationship they did not originally hypothesize. How should they treat this finding?
Explanation
Findings from exploratory analyses carry a higher risk of being due to chance (Type I error). Therefore, the responsible scientific approach is to report them transparently as exploratory and recommend replication to confirm the finding.
Other questions
According to the guidelines for preparing data for analysis, what is a crucial first step regarding raw data and participant identification?
What is the recommended data entry approach for a multiple-response measure, such as a self-esteem scale with ten individual items?
What is the definition of a planned analysis?
What is an exploratory analysis?
Why is it crucial to differentiate planned from exploratory analyses in a research report?
If a researcher finds a response of '12' on a questionnaire that used a 1-to-10 rating scale, what does this specific value represent?
If a researcher justifiably excludes a participant's data from the main analyses, what is the correct procedure for handling that data?
A researcher hypothesizes that there will be a difference between two condition means. After collecting data, they compute the means and standard deviations, make a bar graph, and compute Cohen's d. What type of analysis is this?
Before turning to primary research questions, what is a key preliminary analysis to conduct for a multiple-response measure?
A study on the number of lifetime sexual partners finds that while most students report fewer than 15, a few report scores of 60 or 70. According to the text, what is a plausible interpretation of these high scores?
What is one recommended strategy for dealing with outliers that might be truly extreme responses, not errors?
What is the primary role of descriptive statistics in the context of data analysis?
According to Daryl Bem's suggestion for exploratory analysis, what does he mean by a 'fishing expedition'?
Why is it important to thoroughly understand your results at a descriptive level first, before moving on to inferential statistics?
What is the common format for a data file created in a spreadsheet program like Excel or a statistical program like SPSS?
In a reaction time study, most participants take a few seconds to respond. If one participant takes 3 minutes, why can it be justifiable to exclude this data point?
What is one benefit of entering individual item responses from a scale into a spreadsheet rather than just a total score?
What is a strategy suggested for dealing with outliers when analyzing the data both with and without them?
What is the very first type of analysis that should be performed on each important variable separately, after data is prepared?
Professional researchers usually keep a copy of their raw data and consent forms for several years after a project is complete. What is the primary reason for this practice?
What is the recommended action when an outlier is discovered to be the result of a response being entered incorrectly in the data file?
A researcher expects a correlation between two quantitative variables. According to the description of a planned analysis, what would be the appropriate steps?
If you decide to exclude data, you must apply the exclusion criteria consistently to every response and every participant. What is the reason for this consistency?
The final step in the process before moving to inferential statistics is to ensure you thoroughly understand your results at a descriptive level. What does this understanding entail?
What is the primary danger of a 'fishing expedition' or exploratory analysis?
When checking raw data, a researcher finds several illegible responses for a key dependent variable for one participant. What is the most likely course of action?
A researcher has raw data on paper questionnaires. Before beginning any statistical analysis, what is a recommended practice to ensure data preservation?
What type of variable is NOT necessary to analyze separately during preliminary analyses?
If a researcher reports descriptive statistics showing a scatterplot with an indistinct 'cloud' of points and a Pearson's r of negative 0.02, what should be clear from this information alone?
What is the primary reason to analyze data both including and excluding outliers if the outliers are believed to be genuine extreme scores?
In the sample data file shown in Table 12.6, what does each row represent?
A distribution of self-report happiness ratings on a 1-to-10 scale is unimodal and negatively skewed with a mean of 8.25. What does this indicate about the participants' responses?
What should a researcher report when presenting results where data from some participants has been excluded?
Which phase of data analysis involves assessing the internal consistency of a multiple-response measure using statistics like Cronbach's alpha?
If a researcher decides to analyze the data both including and excluding outliers, and the results differ significantly, what is the recommended action?
Even when you understand the statistics involved, analyzing data can be a complicated process. Which of the following is NOT listed as a form that 'raw' (unanalyzed) data might take?
According to the chapter, what is the ultimate goal of the data analysis process?
What is a major reason why outliers do not necessarily represent an error, misunderstanding, or lack of effort?
If a researcher finds an interesting pattern in the data during an exploratory analysis, what is the most scientifically sound next step?
Which of these steps is part of preparing data for analysis, rather than a preliminary analysis?
When are categorical variables entered as numbers instead of labels in a data file?
What does it mean to have a 'secure location' for storing data, according to the examples provided?
If a researcher suspects an outlier represents a participant's lack of effort, why is exclusion from the analysis often justified?
In the final section 'Understand Your Descriptive Statistics', what is the main point being made about the relationship between descriptive and inferential statistics?
When examining raw data, which of the following issues might require a researcher to decide whether a participant's data is unusable?
A treatment group of 50 participants has a mean score of 34.32, and a control group of 50 has a mean of 21.45, with a very strong Cohen's d of 1.31. What should be clear from these descriptive statistics?
The process of analyzing data is described as complicated because for each of several participants, there are often data for several different variables. Which of the following is NOT listed as a typical variable type?
What is the primary benefit of having data already in a computer file versus on paper?
If a researcher performs an exploratory analysis and finds an interesting result, what should prevent them from simply presenting this as a confirmed finding?