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A low p-value suggests data is inconsistent with the null, potentially favoring an alternative hypothesis. Common significance thresholds are 0.05 or 0.01. The p-value in statistics quantifies the evidence against a null hypothesis.
For a chi-square test, a p-value that is less than or equal to the .05 significance level indicates that the observed values are different to the expected values. Thus, low p-values (p< .05) indicate a likely difference between the theoretical population and the collected sample.
A lower p-value is sometimes interpreted as meaning there is a stronger relationship between two variables. However, statistical significance means that it is unlikely that the null hypothesis is true (less than 5%).
The confidence interval (CI) is a range of values that’s likely to include a population value with a certain degree of confidence. It is often expressed as a % whereby a population mean lies between an upper and lower interval.
A p-value less than 0.05 (typically ≤ 0.05) is statistically significant. It indicates strong evidence against the null hypothesis, as there is less than a 5% probability the results have occurred by random chance rather than a real effect.
The smaller the p-value, the stronger the evidence that you should reject the null hypothesis. The observed value is statistically significant (p ≤ 0.05), so the null hypothesis (N0) is rejected, and the alternative hypothesis (Ha) is accepted.
A z-score is a statistical measure that describes the position of a raw score in terms of its distance from the mean, measured in standard deviation units. A positive z-score indicates that the value lies above the mean, while a negative z-score indicates that the value lies below the mean.
For example, a p-value of 0.01 would mean there is a 1% chance of committing a Type I error. However, using a lower value for alpha means that you will be less likely to detect a true difference if one really exists (thus risking a type II error).
A bell-shaped curve, also known as a normal distribution or Gaussian distribution, is a symmetrical probability distribution in statistics. It represents a graph where the data clusters around the mean, with the highest frequency in the center, and decreases gradually towards the tails.
Hofstede’s Cultural Dimensions Theory, developed by Geert Hofstede, is a framework used to understand the differences in culture across countries. Hofstede’s initial six key dimensions include power distance, uncertainty avoidance, individualism-collectivism, masculinity-femininity, and short vs. long-term orientation.