If you keep in mind the lessons about relationship strength and sample size, the chapter suggests you will often know whether a result is statistically significant based on what information alone?
Explanation
This question highlights a key takeaway: that with practice, one can develop an intuition for statistical significance by simply looking at the effect size and sample size, which are part of the descriptive statistics.
Other questions
According to the text, what is the term for the descriptive summary data, such as means or correlation coefficients, that are computed for a sample?
What is the term for the random variability in a statistic from one sample to another?
What is the primary purpose of null hypothesis testing?
Which statement best describes the null hypothesis (H0)?
What is the alternative hypothesis (H1)?
What is the correct definition of the p-value in null hypothesis testing?
In null hypothesis testing, what is the term for the criterion, almost always set to 0.05, that is used to decide whether to reject the null hypothesis?
A research result is said to be 'statistically significant' when which condition is met?
What is the appropriate terminology when a p-value is greater than 0.05?
According to the chapter, which two factors primarily determine the p-value?
If a study with a sample of three women and three men finds a weak sex difference (Cohen's d = 0.10), what is the likely outcome?
A researcher conducts a study with a very large sample (N = 500) and finds a very weak relationship. Can this result be statistically significant?
What does the term 'practical significance' refer to?
A new treatment for social phobia produces a statistically significant positive effect, but the effect is very small. Why might this result lack 'clinical significance'?
If a researcher finds a p-value of 0.02, what is the most common misinterpretation of this result?
In the general logic of null hypothesis testing, what is the first step?
If the sample relationship would be extremely unlikely if the null hypothesis were true, what is the correct action to take?
How does a stronger sample relationship affect the p-value, assuming sample size is constant?
How does a larger sample size affect the p-value, assuming relationship strength is constant?
The corresponding values of statistics in the population are called what?
Why are sample statistics not considered perfect estimates of their corresponding population parameters?
Any statistical relationship in a sample can be interpreted as either reflecting a real relationship in the population or what other alternative?
If a p-value is 0.45, what does this indicate about the sample result?
A study with 500 women and 500 men finds a strong sex difference with a Cohen's d of 0.50. Why should this result seem 'highly unlikely' if the null hypothesis were true?
What does the chapter suggest as a way to avoid misunderstanding the p-value?
A study on a new medication shows a statistically significant improvement in symptoms (p less than 0.05), but the average improvement is very small. This is an example of a result that has statistical significance but may lack what?
If two studies have the same relationship strength, but Study A has a sample of 20 and Study B has a sample of 200, which study is more likely to have a lower p-value?
If two studies have the same sample size, but Study C has a strong relationship and Study D has a weak relationship, which study is more likely to have a lower p-value?
The chapter mentions the term 'clinical significance' in the context of a study on a new treatment for social phobia. This term is presented as a specific application of what broader concept?
Retaining the null hypothesis means that a researcher concludes what?
If a sample Pearson's r value is -0.29, which of the following is a correct interpretation according to the principles of null hypothesis testing?
Why did Mehl and his colleagues retain the null hypothesis regarding sex differences in talkativeness?
Why did Kanner and his colleagues reject the null hypothesis regarding the relationship between hassles and symptoms?
If a weak relationship is found in a small or medium-sized sample, what does the chapter suggest is almost always the outcome regarding statistical significance?
If a strong relationship is found in a medium or large-sized sample, what does the chapter suggest is almost always the outcome regarding statistical significance?
What is the primary researcher's goal when analyzing data from a sample?
The chapter gives an example of the mean number of depressive symptoms being 8.73, 6.45, and 9.44 in three different samples from the same population. This variation is an illustration of what concept?
An informal way of stating the null hypothesis is that the sample relationship 'occurred by...' what?
If a sample relationship would not be extremely unlikely under the null hypothesis, what is the correct decision?
Why is it important to distinguish between statistical significance and practical significance?
What combination of relationship strength and sample size is LEAST likely to produce a statistically significant result?
According to Janet Shibley Hyde's argument mentioned in the chapter, the statistically significant differences between women and men in mathematical problem solving are actually quite what?
If a researcher develops an intuitive judgment about whether a result will be statistically significant based on descriptive statistics alone, what does the chapter suggest this indicates?
The chapter states that a researcher wants to use a sample statistic (e.g., the mean number of symptoms for a sample) to draw conclusions about what?
What is the third and final step in the general logic of null hypothesis testing as outlined in the chapter?
The word 'significant' in the term 'statistically significant' can cause people to interpret differences as strong and important. The chapter uses Janet Shibley Hyde's argument about what topic to illustrate this problem?
The text states that the term 'error' in 'sampling error' refers to what?
When a researcher states a p-value of 0.02 means there is a 98 percent chance the result reflects a real relationship, this is described in the chapter as what?
What two considerations trade off against each other in determining if a result is statistically significant, as described in the text?